Hybrid Tracking System

ABSTRACT

A surgical navigation module comprising: (a) a microcomputer; (b) a tri-axial accelerometer; (c) a tri-axial gyroscope; (d) at least three tri-axial magnetometers; (e) a communication module; (f) an ultrawide band transceiver; and, (g) at least four ultrawide band antennas.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application claims the benefit of U.S. Provisional PatentApplication Ser. No. 62/022,899, entitled, “CRANIUM AND POSTCRANIAL BONEAND SOFT TISSUE RECONSTRUCTION,” filed Jul. 10, 2014, the disclosure ofeach of which is incorporated herein by reference.

RELATED ART Field of the Invention

The present disclosure is directed to various aspects of positionaltracking or navigation of objects in three dimensional space andincludes exemplary applications such as surgical navigation.

Introduction to the Invention

The present disclosure includes a navigation system using aself-reference hybrid navigation system based on the UltraWide Band(UWB) and inertial technologies to provide translational and rotationalnavigation. In contrast, current navigation systems use an optical, anelectromagnetic (EM), or an inertial navigation system.

The primary challenge with optical surgical navigation systems in thecontext of surgical navigation is the line-of-sight (LOS) requirementbetween the camera and the tracking modules, which is often obstructedby the surgeons or the surgical technicians during surgery. The patientregistration error for the camera can also introduce substantial errorin the system.

Current electromagnetic (EM) navigation systems as part of surgicalnavigation use an EM generator and an EM probe to track motion. The EMnavigation does not suffer from line-of-sight requirement, however, theaccuracy of the system is reduced with metallic objects in the vicinityof the probe. This is a common problem in many surgeries when multiplemetallic equipment parts are utilized, such as metal retractors.

Lastly, current inertial surgical navigation systems as part of surgicalnavigation use a set of inertial sensors (accelerometers and gyroscopes)to comprise the active navigation unit. Inertial navigation systems arenot accurate for translation navigation without external observationinputs, such as a global positioning system (GPS) or an opticalnavigation system to correct for arithmetic drifting. Inertialnavigation is also limited to orientation navigation, and can beinaccurate from ferromagnetic, martensitic material, or permanent magnetdistortion.

The current typical use of the ultra side band (UWB) localization is forasset and personnel tracking, where a multitude of anchors or basestations are setup within a facility, and where an UWB tag is attachedto each tracking asset. A first localization method of many current UWBsystems uses time of arrival (TOA), which is optimal for large areatracking. However, the tracking accuracy of a TOA system is in the rangeof meters, which is not suitable for high accuracy surgicalapplications. A second UWB localization method uses the time differenceof arrival (TDOA), which has the potential for high accuracy trackingapplications. However, the implementation of a TDOA system issubstantially more challenging than TOA system. In particular, theaccuracy of the TDOA system hinges on providing a coherent clocksynchronization, and clock jitter, and drift mitigation. But to date, noTDOA system is reliable for surgical navigation. Specifically, currentUWB systems do not have optimized antenna for their intendedapplications, and do not account for orientation dependencies caused byantenna polarization. For high accuracy medical applications, the phasecenter error of a series of optimized antennas must be characterized andmitigated. Yet most UWB localization systems do not account for harshindoor environments having numerous multipaths and the potential fornon-line-of-sight conditions, both of which are common in surgicalenvironments (e.g., operating rooms and surgical suites). Moreover, thedeployment of current UWB localization methods also suffer fromlimitations such as strict anchor configurations and installation,tedious calibration procedures, and inaccuracy from incoherent clockbetween anchors.

Combining an UWB system with an inertial measurement unit (IMU) systemovercomes many, if not all, of the foregoing problems presuming certainissues are addressed. For the UWB system, calibration and installationof multiple anchors remains a primary challenge in achieving highaccuracy. Incoherent clock synchronization among the anchors introducesuncertainty to the localization results. Current UWB systems are limitedto the update rate set by the manufacturer, so that real time surgicalnavigation is not possible. For the IMU system, drifting on the headingremains a primary concern as these systems do not use magnetometers forheading navigation correction. In previous unsuccessful attempts tocombine UWB and IMU systems, these systems were treated as completelyseparate entities that did not interact with each other or make use ofthe sparse translational data from the UWB system to correct thetranslation estimation from the IMU system. One significant reason thatorientation tracking is not complemented between the two systems is thata single UWB tag is incapable of producing orientations information.Rather, it takes a minimum of three UWB tags on the same rigid body togenerate orientation information.

The current UWB and IMU hybrid tracking system, as disclosed in moredetail hereafter, addresses these deficiencies and allows for precisetracking and may be used in the context of surgical navigation.

It is a first aspect of the present invention to provide a surgicalnavigation system comprising a signal receiver communicatively coupledto a primary processor, the primary processor programmed to utilize asequential Monte Carlo algorithm to calculate changes in threedimensional position of an inertial measurement unit mounted to asurgical tool, the processor communicatively coupled to a first memorystoring tool data unique to each of a plurality of surgical tools, and asecond memory storing a model data sufficient to construct a threedimensional model of an anatomical feature, the primary processorcommunicatively coupled to a display providing visual feedback regardingthe three dimensional position of the surgical tool with respect to theanatomical feature.

In a more detailed embodiment of the first aspect, the surgicalnavigation system further includes a reference inertial measurement unitcommunicatively coupled to a first on-board processor and a firstwireless transmitter to transmit data to the primary processor, thereference inertial measurement unit configured to be attached to theanatomical feature, where the first on-board processor directstransmission of data from the reference inertial measurement unit to thefirst wireless transmitter, where the inertial measurement unit mountedto the surgical tool comprises a utility inertial measurement unitcommunicatively coupled to a second on-board processor and a secondwireless transmitter, the second on-board processor configured to bemounted to one of the plurality of surgical tools, and where the primaryprocessor is communicatively coupled to a primary received configured toreceive data from the first wireless transmitter and data from thesecond wireless transmitter. In yet another more detailed embodiment,the second on-board processor directs communication via the secondwireless transmitter of an identity of the surgical tool to which theutility inertial measurement unit is mounted. In a further detailedembodiment, the inertial measurement unit includes at least threeaccelerometers and three magnetometers, each of the at least threeaccelerometers outputs data relative to three axes for a total of noless than nine accelerometer data streams, each of at least threemagnetometers outputs data relative to three axes for a total of no lessthan nine magnetometer data streams, the primary processor utilizes thenine accelerometer data streams and the nine magnetometer data streamsto calculate changes in three dimensional position of the inertialmeasurement unit mounted to the surgical tool. In still a furtherdetailed embodiment, the model data stored in the second memory includesa three dimensional virtual model of the anatomical feature, the tooldata stored in the first memory includes three dimensional virtualmodels of the plurality of surgical tools, the display displays thethree dimensional virtual model of the anatomical feature, the displaydisplays a three dimensional virtual model of the surgical tool, theprimary processor is operative to utilize data from the referenceinertial measurement unit to reposition the three dimensional virtualmodel of the anatomical feature, and the primary processor is operativeto utilize data from the utility inertial measurement unit to repositionthe three dimensional virtual model of the surgical tool. In a moredetailed embodiment, the primary processor is operative to utilize datafrom the inertial measurement unit to reposition the three dimensionalvirtual model of the surgical tool with respect to a three dimensionalvirtual model of the anatomical feature in real-time. In a more detailedembodiment, the sequential Monte Carlo algorithm includes a vonMises-Fisher density algorithm component. In another more detailedembodiment, the tool data stored in the first memory includes positionaldata indicating the relative distances between an end effector of thesurgical tool and a mounting location on the surgical device for theinertial measurement unit, and the surgical tool includes at least oneof a reamer, a cup positioned, an impacter, a drill, a saw, and acutting guide. In yet another more detailed embodiment, the inertialmeasurement unit includes at least three magnetometers, and the displayis at least one of coupled to the surgical tool or coupled to theprimary processor.

It is a second aspect of the present invention to provide a calibrationsystem, for an inertial measurement unit including a magnetometer and anaccelerometer, comprising: (a) a primary platform rotationallyrepositionable with respect to an intermediate platform along a firstaxis; (b) a final platform rotationally repositionable with respect tothe intermediate platform along a second axis, the second axis beingperpendicular to the first axis, the final platform including a retainerconfigured to mount to an inertial measurement unit; and, (c) aprocessor and associated software configured to communicatively coupleto the inertial measurement unit, the software operative to utilize dataoutput from a magnetometer associated with the inertial measurement unitwhile the primary platform is rotated with respect to the intermediateplatform and while the final platform is rotated with respect to theintermediate platform and record a data set resembling an ellipsoid, thesoftware operative to fit a sphere to the data set and generatemagnetometer correction calculations to account for distortions in alocal magnetic field, thereby normalizing future data output from themagnetometer.

In a more detailed embodiment of the second aspect, the primary platformis stationary. In yet another more detailed embodiment, the primaryplatform at least partially houses a motor configured to cause rotationof the intermediate platform with respect to the primary platform. In afurther detailed embodiment, the software is operative to utilize afirst set of data output from an accelerometer associated with theinertial measurement unit while the inertial measurement unit is at afirst stationary position and operative to utilize a second set of dataoutput from the accelerometer at a second stationary position differentfrom the first stationary position to generate accelerometer correctioncalculations to normalizing future data output from the accelerometer.In still a further detailed embodiment, the first stationary positioncorresponds to the primary platform being at a first fixed position withrespect to the intermediate platform and the final platform is at asecond fixed position with respect to the intermediate platform, and thesecond stationary position corresponds to at least one of the primaryplatform being at a third fixed position with respect to theintermediate platform and the final platform is at a fourth fixedposition with respect to the intermediate platform. In a more detailedembodiment, the final platform includes a plurality of retainers, whereeach of the plurality of retainers is configured to mount to at leastone of a plurality of inertial measurement units.

It is a third aspect of the present invention to provide a method ofcalibrating an inertial measurement unit including a magnetometer, themethod comprising: (a) rotating a first inertial measurement unit, whichincludes a first inertial measurement unit, about a first rotationalaxis and a second rotational axis, the first rotational axis beingperpendicular to the second rotational axis, while concurrentlyreceiving raw local magnetic field data from the first magnetometer; (b)applying a uniform calculation to the raw local magnetic field data tocalculate a distortion in a local magnetic field; and, (c) normalizingthe raw local magnetic field data received from the magnetometer byaccounting for a calculated distortion in the local magnetic field toprovide refined local magnetic field data.

In a more detailed embodiment of the third aspect, the first inertialmeasurement unit includes a first accelerometer, the method furthercomprises: (i) holding stationary the first inertial measurement unit ina first three dimensional position while concurrently receiving rawaccelerometer data from the first accelerometer; (ii) holding stationarythe first inertial measurement unit in a second three dimensionalposition while concurrently receiving raw accelerometer data from thefirst accelerometer, the second three dimensional position beingdifferent than the first three dimensional position; and, (iii)normalizing data received from the first accelerometer to reflect zeroacceleration when the first accelerometer is stationary. In yet anothermore detailed embodiment, the first inertial measurement unit includes asecond accelerometer, the method further comprises: (i) holdingstationary the second inertial measurement unit, as the firstaccelerometer is held stationary, in a third three dimensional positionwhile concurrently receiving raw accelerometer data from the secondaccelerometer; (ii) holding stationary the second inertial measurementunit, as the first accelerometer is held stationary, in a fourth threedimensional position while concurrently receiving raw accelerometer datafrom the second accelerometer, the fourth three dimensional positionbeing different than the third three dimensional position; and, (iii)normalizing data received from the second accelerometer to reflect zeroacceleration when the second accelerometer is stationary. In a furtherdetailed embodiment, the raw local magnetic field data is representativeof an ellipsoid in three dimensions, and the refined local magneticfield data is representative of a sphere in three dimensions. In still afurther detailed embodiment, the uniform calculation includes fitting asphere to the raw local magnetic field data, and normalizing the rawlocal magnetic field data includes subtracting the calculated distortionfrom the raw local magnetic field data to provide refined local magneticfield data. In a more detailed embodiment, the method further comprisesa second inertial measurement unit having its own first accelerometer.In a more detailed embodiment, the second inertial measurement unit hasits own first accelerometer.

It is a fourth aspect of the present invention to provide a method ofidentifying a surgical tool when coupled to an inertial measurementunit, the method comprising: (a) mounting an inertial measurement unitto one of a plurality of surgical tools, each of the plurality ofsurgical tools having a unique interface; and, (b) reading the uniqueinterface to transmit a signal to a processor communicatively coupled tothe inertial measurement unit to identify one of the plurality ofsurgical tools responsive to reading the unique interface.

In a more detailed embodiment of the fourth aspect, the inertialmeasurement unit is operatively coupled to a plurality of switches, theunique interface engages at least one of the plurality of switches, andthe step of reading the unique interface includes a determination by theprocessor as to which of the plurality of switches have been engaged bythe unique interface. In yet another more detailed embodiment, theprocessor is coupled to the inertial measurement unit, and the processorand inertial measurement unit are housed within a common housing. In afurther detailed embodiment, the processor is remote from the inertialmeasurement unit, and the processor and inertial measurement unit arenot housed within a common housing.

It is a fifth aspect of the present invention to provide a method ofconducting surgical navigation comprising: (a) utilizing a plurality ofinertial measurement units to generate acceleration data and magneticdata; (b) calibrating the plurality of inertial measurement units inproximity to a surgical procedure location; (c) registering relativelocations of a first and second inertial measurement units comprisingthe plurality of inertial measurement units, where registering relativelocations includes mounting the first inertial measurement unit to aregistration tool that uniquely engages a patient's anatomy in aparticular location and orientation, and where registering the relativelocations includes mounting the second inertial measurement unit to thepatient; (d) attaching the first inertial measurement unit to a surgicaltool post registration; (e) repositioning the surgical tool and thefirst inertial measurement unit toward a surgical site associated withthe patient's anatomy; and, (f) providing visual feedback regarding atleast one of a location and an orientation of the surgical tool when atleast one of the patient's anatomy is not visible or an operative end ofthe surgical tool is not visible.

It is a sixth aspect of the present invention to provide a method ofconducting surgical navigation comprising: (a) utilizing a plurality ofinertial measurement units to generate acceleration data and magneticdata; (b) calibrating the plurality of inertial measurement units inproximity to a surgical procedure location; (c) registering relativelocations of a first and second inertial measurement units comprisingthe plurality of inertial measurement units, where registering relativelocations includes mounting the first inertial measurement unit to aregistration tool that uniquely engages a patient's anatomy in aparticular location and orientation, and where registering the relativelocations includes mounting the second inertial measurement unit to thepatient; (d) attaching the first inertial measurement unit to a surgicaltool post registration; (e) repositioning the surgical tool and thefirst inertial measurement unit toward a surgical site associated withthe patient's anatomy; and, (f) providing visual feedback regarding alocation and an orientation of the surgical tool with respect to apredetermined surgical plan, where the predetermined surgical planidentifies at least one of a permissible range of locations and apermissible range of orientations the surgical tool may occupy.

It is a seventh aspect of the present invention to provide a method ofgenerating a trauma plate for a particular bone, the method comprising:(a) accessing a database comprising a plurality of three dimensionalbone models of a particular bone; (b) assessing features comprising atleast one of longitudinal contours and cross-sectional contours for eachof the plurality of three dimensional bone models, where thelongitudinal contours are taken along a dominant dimension of theplurality of three dimensional bone models; (c) clustering the pluralityof three dimensional bone models based upon the assessed features togenerate a plurality of clusters, where the plurality of clusters isnumerically less than ten percent of the plurality of three dimensionalbone models; and, (d) generating a trauma plate for each of theplurality of clusters.

In a more detailed embodiment of the seventh aspect, generating a traumaplate for each of the plurality of clusters includes selection offixation locations to avoid soft tissue attachments to the particularbone. In yet another more detailed embodiment, the plurality of threedimensional bone models include at least one commonality, wherein thecommonality comprises at least one of sex, ethnicity, age range, andheight range. In a further detailed embodiment, generating the traumaplate for each of the plurality of clusters includes incorporating atleast one of a mean longitudinal contour and a mean cross-sectionalcontour for that particular cluster.

It is an eighth aspect of the present invention to provide a method ofgenerating a patient-specific trauma plate for a particular bone, themethod comprising: (a) obtaining patient-specific image data for aparticular bone having been injured or degenerated; (b) using thepatient-specific image data to analyze at least one of those portions ofthe particular bone absent and those portions of the particular bonepresent; (c) generating a patient-specific virtual bone model of theparticular bone in a unified state that includes bone not visible in thepatient-specific image data; (d) assessing the contours of thepatient-specific virtual bone model; and, (e) generating apatient-specific trauma plate using the patient-specific virtual bonemodel.

It is a ninth aspect of the present invention to provide a method ofkinematically tracking motion of a patient's anatomy using inertialmeasurement units, the method comprising: (a) mounting a first inertialmeasurement unit to an exterior of a patient's first anatomical featureof interest; (b) mounting a second inertial measurement unit to anexterior of a patient's second anatomical feature of interest; (c)registering a position of the patient's first anatomical feature with avirtual model of the patient's first anatomical feature of interestusing the first inertial measurement unit; (d) registering a position ofthe patient's second anatomical feature with a virtual model of thepatient's second anatomical feature of interest using the secondinertial measurement unit; (e) dynamically correlating the position ofthe patient's first anatomical feature of interest with a virtual modelof the first anatomical feature using the first inertial measurementunit; and, (f) dynamically correlating the position of the patient'ssecond anatomical feature of interest with a virtual model of the secondanatomical feature using the second inertial measurement unit.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of an overall process of generating masscustomized and patient-specific molds from a partial anatomy.

FIG. 2 is a schematic diagram detailing how to add a new anatomicalstructure to a statistical atlas in order to generate correspondence.

FIG. 3 is a multi-resolution 3D registration algorithm overviewcorresponding to the multi-resolution 3D registration in FIG. 2.

FIG. 4 is a multi-scale registration of feature points using multi-scalefeatures.

FIG. 5 is a low level break down of multi-resolution registration asoutlined in FIG. 3.

FIG. 6 is a graphical representation of capturing variation inpopulation upon generation of correspondence

FIG. 7 is a schematic diagram of a full bone reconstruction processusing partial, deformed or shattered anatomy.

FIG. 8 is a schematic diagram of a defect classification process forgeneration of defect templates.

FIG. 9 is a graphical example of existing AAOS classifications foracetabular defects.

FIG. 10 is a graphical example of existing Paprosky acetabular defectclassifications.

FIG. 11 is a graphical example of existing Paprosky acetabular defectsubclassifications.

FIG. 12 is a table and associated drawings showing the results ofreconstruction on a pelvis for different defects, which is an exemplaryapplication and validation of the full bone reconstruction depicted inFIG. 7.

FIG. 13 is a distance map for the mean RMS error of reconstruction on apelvis for different defects, which validates the accuracy of the fullbone reconstruction depicted in FIG. 7.

FIG. 14 is a three dimensional model representation of a patient withsevere pelvis discontinuity on the left. On the right is an example ofthe three dimensional model of the patient's pelvis shown on the left.

FIG. 15 is a comparison of the reconstructed left model and the originalpatient model, as well as right and left anatomy.

FIG. 16 is a distance map between a reconstructed model and a mirrorimage of the pelvis model reconstructed.

FIG. 17 is a patient with complete pelvis discontinuity and results ofreconstruction with RMS error of 1.8 mm.

FIG. 18 are the results of reconstruction on partial skulls and meandistance map for reconstruction error.

FIG. 19 are the results of reconstruction of a shattered femur.

FIG. 20 is a schematic diagram of the process of creating apatient-specific reconstructive implant.

FIG. 21 is a schematic diagram of the process for implant generationdepicted in FIG. 20.

FIG. 22 is a process flow diagram showing various steps forreconstruction of patient full anatomy from partial anatomy andgeneration of patient specific cup implant for pelvis discontinuity.

FIG. 23 is a graphical representation of a patient-specific placementguide for a patient-specific acetabular implant.

FIG. 24 comprises images studying the relationship between the threeattachment sites of an implant and the cup orientation for masscustomization.

FIG. 25 comprises images showing the sequence for mass customization ofacetabular cages in accordance with the instant disclosure.

FIG. 26 is a schematic diagram for a method for manufacturing a massproduced custom acetabular component using a modular design.

FIG. 27 is a schematic diagram of a process for generating apatient-specific hip stem for reconstructive surgeries.

FIG. 28 is a schematic diagram of a process for mass customized implantgeneration.

FIG. 29 is a schematic diagram depicting a process for using astatistical atlas for generation of both mass customized andpatient-specific hip implants.

FIG. 30 is a schematic diagram depicting a process for using astatistical atlas for generation of both mass customized andpatient-specific hip implant.

FIG. 31 is a schematic diagram depicting an outline of a process fordesigning population specific hip stem components.

FIG. 32 is a graphical representation showing where the proximal femurlandmarks are located.

FIG. 33 is a 3D model of a femur showing canal waist in the middle ofthe femur and femur waist along the length of the femur.

FIG. 34 is a graphical representation showing where the proximal femuraxes are located.

FIG. 35 is a graphical representation showing where the neck centercalculation is located.

FIG. 36 is a graphical representation of two points used to define afemur proximal anatomical axis.

FIG. 37 is a graphical representation of 3D proximal femur measurements.

FIG. 38 shows an exemplary Dorr ratio, which is generally in 2D (fromXR).

FIG. 39 is a graphical representation of the B/A ratio at the IMIsthmus.

FIG. 40 is a graphical representation of IM canal measurements.

FIG. 41 is a contour and a fitted circle.

FIG. 42 is a graphical representation of the measurements taken toobtain the IM canal femur radii ratio.

FIG. 43 depicts two femur models showing the effect of the change in theradii ratio, with the one on the left having a radii ratio of 0.69, andthe one on the right having a radii ratio of 0.38.

FIG. 44 is a plot of BMD versus RRFW for males and females, as well asthe best line fit for each data set (male, female).

FIG. 45 is a graphical representation of medial contours, neck axis andhead point of a proximal femur before alignment.

FIG. 46 is a graphical representation of an anatomical axis alignmentwith the Z-direction.

FIG. 47 is a graphical representation of medial contours aligned usingthe femoral neck pivot point.

FIG. 48 is a graphical representation of different models generatedusing interpolation between models to show the smoothness ofinterpolation.

FIG. 49 is a graphical and pictorial representation of three dimensionalmapping of bone density.

FIG. 50 is an X-ray depiction shown the IM width at 3 levels, and theproximal axis, head offset and femur head.

FIG. 51 is a plot of proximal angle versus head offset.

FIG. 52 is a plot of proximal angle versus head height.

FIG. 53 is a plot of head offset versus head height.

FIG. 54 is a proximal angle histogram.

FIG. 55 is a plot depicting clusters of females and males for headoffset and calcar diameter.

FIG. 56 is a plot depicting clusters of females and males for headoffset and proximal angle.

FIG. 57 is a head offset histogram.

FIG. 58 is an IM sizes histogram.

FIG. 59 is a graphical representation of female measurements withrespect to a proximal femur.

FIG. 60 is a graphical representation of male measurements with respectto a proximal femur.

FIG. 61 is a graphical representation of female measurements withrespect to the greater trochanter height.

FIG. 62 is a graphical representation of male measurements with respectto the greater trochanter height.

FIG. 63 is a graphical representation and table showing intramedullarycanal shape differences between males and females.

FIG. 64 depicts a female femur and intramedullary canal representativeof normal bone density and quality.

FIG. 65 depicts a female femur and intramedullary canal representativeof less than normal bone density and quality.

FIG. 66 depicts a female femur and intramedullary canal representativeof osteoporosis.

FIG. 67 is a chart comprising an interpolated dataset headoffsetshistogram.

FIG. 68 is a chart comprising a canal size dataset histogram.

FIG. 69 depicts medial contours and head centers distribution forvarious femur groups.

FIG. 70 is a plot showing headoffset distribution for a particular sizefemur group.

FIG. 71 is a table reflecting anteversion angle measurements for malesand females.

FIG. 72 is a picture depicting how anterior-posterior height ismeasured.

FIG. 73 is a plot of heat height versus anterior-posterior head heightfor a femur relative to its pivot point for males and females, whichincludes a linear best fit through each data set (male, female).

FIG. 74 is a plot of heat height versus anterior-posterior head heightfor a femur relative to its anatomical axis mid-point for males andfemales, which includes a linear best fit through each data set (male,female).

FIG. 75 is a graphical depiction of parameters utilized for creation ofhip stem implant families on a gender and/or ethnicity basis inaccordance with the instant disclosure that leads to mass custom implantshape parameters for a femoral stem component extracted from clustering.

FIG. 76 depicts a primary hip stem in both assembled and exploded views.

FIG. 77 depicts a revision hip stem in both assembled and explodedviews.

FIG. 78 is a graphical representation of surface points and utilizationof a plane to isolate an acetabular cup geometry in accordance with theinstant disclosure.

FIG. 79 graphically depicts a plurality of virtual, 3D acetabular cupanatomical templates created in accordance with the instant disclosure.

FIG. 80 graphically depicts an anatomical acetabular cup and femoralstem ball shape exhibiting multiple cup radii.

FIG. 81 is a two dimensional depiction of curvature matching between theacetabular cup and femoral head.

FIG. 82 is a graphical depiction of mapped contours of a pelvis used tocross-sectionally analyze the acetabular cup.

FIG. 83 is a graphical depiction of automatic detection of thetransverse acetabular ligament pursuant to the instant disclosure a asmethod for determining acetabular implant cup orientation.

FIG. 84 is a graphical depiction of the sequence for extracting porousshapes and sizes to match a patient's bone anatomy from microcomputerized tomography scans of the patient.

FIG. 85 is an exemplary process diagram for creating pet specificimplants and cutting guides in accordance with the instant disclosure.

FIG. 86 is an exemplary process diagram for creating mass customizedorthopedic implants for pets using statistical atlases in accordancewith the instant disclosure.

FIG. 87 is an exemplary process diagram for generating patient specificcutting and placement devices for implant systems in accordance with theinstant disclosure.

FIG. 88 is an exemplary process diagram for non-rigid registration fromFIG. 87 and creation of patient specific three dimensional pelvis andproximal femur models from X-rays in accordance with the instantdisclosure.

FIG. 89 are pictures and multiple X-ray views used for reconstruction ofpelvis and proximal femur in accordance with the instant disclosure.

FIG. 90 is an exemplary process diagram for automatic segmentation ofpelvis and proximal femur from MM and CT scans, as described in FIG. 87.

FIG. 91 is an exemplary process diagram for automatic segmentation ofcomplex and shattered anatomy from MRI or CT scans, as outlined in FIG.87.

FIG. 92 is an exemplary process diagram for virtual templating both anacetabular cup and a femoral stem used with a hip replacement procedure.

FIG. 93 is an exemplary process diagram for automatic femoral stemplacement using distal fixation, which is a specific example of thegeneral process outlined in FIG. 92.

FIG. 94 is an exemplary process diagram for automatic femoral stemplacement using press fit and three contacts, which is a specificexample of the general process outlined in FIG. 92.

FIG. 95 is a graphical depiction of automatic pelvis landmarking inaccordance with the instant disclosure.

FIG. 96 is a graphical depiction of automatic cup orientation andplacement in accordance with the instant disclosure.

FIG. 97 comprises a series of X-rays overlaid with measurement andcalculation data for acetabular cup and femoral stem placementevaluation in accordance with the instant disclosure.

FIG. 98 is a graphical depiction of an assessment of acetabular cup andfemoral stem placement to ensure overall limb length restoration andorientation in accordance with the instant disclosure.

FIG. 99 is a screenshot of a preplanning interface for evaluating andmodifying implant placement and sizing in accordance with the instantdisclosure.

FIG. 100 comprises a series of sequential images depicting an exemplaryprocess for using patient specific tools for resection and placement ofa femoral stem.

FIG. 101 comprises a series of sequential images depicting an exemplaryprocess for using patient specific guide for reaming and placement of anacetabular cup.

FIG. 102 depicts a series of 3D virtual maps of acetabulums that may beused for generating patient specific tools and locking mechanism inaccordance with the instant disclosure.

FIG. 103 is an exemplary process diagram for using inertial measurementunits as part of surgical navigation during a hip replacement procedure.

FIG. 104 is a series of sequential images depicting an exemplary processfor using inertial measurement units as part of surgical navigationduring a hip replacement procedure.

FIG. 105 is a series of sequential images depicting an exemplary processfor using inertial measurement units as part of surgical navigationspecific to the femur during a hip replacement procedure.

FIG. 106 graphically depicts an exemplary tool and process forcalibrating the position of an inertial measurement unit for use inlater surgical navigation specific to the pelvis during a hipreplacement procedure.

FIG. 107 is an exemplary process flow diagram for preparing to use andusing inertial measurement units during a surgical procedure, as well asusing inertial measurement after completion of the surgical procedure toevaluate the surgical outcome.

FIG. 108 depicts a series of images showing an inertial measurement unitpod/housing mounted to various tools as part of facilitating surgicalnavigation during a surgical procedure.

FIG. 109 depicts a series of images showing an inertial measurement unit(IMU) pod/housing, a picture of calibrating the IMU as to position withrespect to the patient's anatomy; a picture showing utilization of theIMU pod to surgically navigate a reamer, and finally a picture showingutilization of the IMU pod to surgically navigate an acetabular cupimpacter.

FIG. 110 depicts a picture in picture showing utilization of the IMU podwith an acetabular cup impacter as well as a graphical interface (insetpicture) showing a model of the patient's anatomy (in this case, apelvis) and the distal end of the impacter color coded to confirm thatthe orientation of the impacter is consistent with the surgicalpre-planning orientation.

FIG. 111 is a picture of an IMU utilized in accordance with the instantdisclosure along with a reference ruler for characterizing the relativedimensions of the IMU.

FIG. 112 is an exemplary process flow diagram for creating trauma platesand fixation devices for a given population in accordance with theinstant disclosure.

FIG. 113 is a graphical image from a mean bone showing localized pointson the bone surface that are localized across a population of bones in astatistical atlas in order to define the shape of a bone or traumaplate.

FIG. 114 is a graphical image of a bone showing propagation of plateloci on an entire population, here shown on a single instance.

FIG. 115 is a graphical image showing extraction of bone/trauma platemidline curve post propagation of plate loci.

FIG. 116 is a graphical depiction of the results of computing 3D radiiof curvature (parameters) for a trauma plate midline curve.

FIG. 117 is a graphical depiction showing how the length of the traumaplate is calculated post propagation of plate loci.

FIG. 118 is a graphical depiction showing how the mid-plate width of thetrauma plate is calculated post propagation of plate loci.

FIG. 119 is a graphical depiction showing how the plate cross sectionalradii of the trauma plate is calculated post propagation of plate loci .. . .

FIG. 120 showing plots of plate size data utilized to determine theoptimal number of clusters.

FIG. 121 includes 2D and 3D plots of plate size data utilized togenerate clusters (identified in FIG. 111 as “Clustering”).

FIG. 122 depicts numerous images reflecting parameterization of platesizes (identified in FIG. 111 as “Parameterized Curves” and “GenerateModels”).

FIG. 123 is an exemplary image showing a bone/trauma plate for aparticular cluster being fit to one of the bone models from the clusterto evaluate conformity/fitting to the population.

FIG. 124 is a 3D surface distance map reflecting the spacing between theunderside of the bone/trauma plate surface and the surface of the bonemodel selected for evaluating plate fit.

FIG. 125 depicts validation of designed plate on cadaver to avoid muscleand ligament impingement.

FIG. 126 is an exemplary diagram reflecting the interaction betweenelements of an exemplary patient-fit clavicle trauma system inaccordance with the instant disclosure.

FIG. 127 is an exemplary process flow diagram for the pre-planningelement depicted in FIG. 126.

FIG. 128 is an exemplary process flow diagram for the intra-operativeguidance depicted in FIG. 126, in this case using fluoroscopy.

FIG. 129 is a fluoroscopic image of a clavicle adjacent to anillustration of a clavicle from a top view with partial surroundinganatomy.

FIG. 130 is an exemplary process flow diagram for the intra-operativeguidance depicted in FIG. 126, in this case using ultrasound.

FIG. 131 is a graphical representation matched to X-rays or fluoroscopicimages taken during a range of motion, as well as a plot showing apost-operative evaluation of shoulder kinematics using one or moreinertial measurement units.

FIG. 132 is a pair of three dimensional illustrations of a clavicle withsurrounding anatomy.

FIG. 133 depicts two different views of a clavicle bone model and pointsalong the bone model utilized to identify the clavicle midlinecurvature.

FIG. 134 is depicts a clavicle bone model and locations on the bonemodel where muscle is attached.

FIG. 135 depicts a series of surfaces maps of male and female meanclavicle models across a given population and the degree of shapedifferences across each population.

FIG. 136 is a pair of three dimensional illustrations of a claviclecorrelating contour differences with the muscle attachment sites.

FIG. 137 is a series of cross-sections of a clavicles taken across maleand female populations that shows the contour differences in theclavicle at the various muscle attachment sites.

FIG. 138 is a series of cross-sections of a clavicles taken across maleand female populations that shows the contour differences in theclavicle along the length of the clavicle.

FIG. 139 depicts left and right clavicle models generated responsive topopulation data in a statistical atlas reflecting morphologicaldifferences between left and right clavicles.

FIG. 140 depicts a clavicle bone model to which is fit a superiorlateral plate (left), plate midline curve (center), and midline platecurvature showing radius of curvature (right) in accordance with theinstant disclosure.

FIG. 141 is a chart depicting superior lateral plate clusters forclavicle male and female populations and Table 1 includes data relatingto the same.

FIG. 142 depicts a clavicle bone model to which is fit an anteriormid-shaft 7h plate (left), plate midline curve (center), and midlineplate curvature showing single radius of curvature (right) in accordancewith the instant disclosure.

FIG. 143 is a chart depicting anterior mid-shaft 7h plate clusters forclavicle male and female populations and Table 2 includes data relatingto the same.

FIG. 144 depicts a clavicle bone model to which is fit a superiormid-shaft plate (left), plate midline curve (center), and midline platecurvature showing differing radii of curvature (right) in accordancewith the instant disclosure.

FIG. 145 is a chart depicting superior mid-shaft plate clusters forclavicle male and female populations and Table 3 includes data relatingto the same.

FIG. 146 depicts a clavicle bone model to which is fit an anteriorlateral plate (left), plate midline curve (center), and midline platecurvature showing differing radii of curvature (right) in accordancewith the instant disclosure.

FIG. 147 is a chart depicting anterior lateral plate clusters forclavicle male and female populations and Table 4 includes data relatingto the same.

FIG. 148 depicts a clavicle bone model to which is fit an anteriormid-shaft long plate (left), plate midline curve (center), and midlineplate curvature showing differing radii of curvature (right) inaccordance with the instant disclosure.

FIG. 149 is a chart depicting anterior mid-shaft plate clusters forclavicle male and female populations and Table 5 includes data relatingto the same.

FIG. 150 is an exemplary process flow diagram for generating customizedplate placement guides for trauma reconstructive surgeries in accordancewith the instant disclosure.

FIG. 151 is an exemplary process flow diagram for generating customizedcutting and placement guide for reconstructive surgeries using bonegrafts in accordance with the instant disclosure.

FIG. 152 is an exemplary process flow diagram for generating traumaplate templates and placement tools in accordance with the instantdisclosure.

FIG. 153 is an exemplary process flow diagram for generating hiprevision cage templates and placement tools in accordance with theinstant disclosure.

FIG. 154 is an exemplary process flow diagram for soft tissue andkinematic tracking of body anatomy using inertial measurement units inaccordance with the instant disclosure.

FIG. 155 comprises a pair of screen shots showing a kinematic softwareinterface that identifies soft tissue locations on bone models andtracks soft tissue deformity in accordance with the instant disclosure.

FIG. 156 comprises bone models of the femur, tibia, and fibula depictingpoints on the respective bone models where ligaments (MCL, LCL) areattached, where the points are color coded to identify points of higheror lower likelihood of ligament attachment.

FIG. 157 comprises a bone model of the distal femur depicting points onthe respective bone models where ligaments (ACL, PCL) are attached,where the points are color coded to identify points of higher or lowerlikelihood of ligament attachment.

FIG. 158 comprises a bone model of the proximal tibia depicting pointson the respective bone models where ligaments (ACL, PCL) are attached,where the points are color coded to identify points of higher or lowerlikelihood of ligament attachment.

FIG. 159 depicts front, rear, and two side views of a 3D virtual modelof a knee joint that includes ligament attachment in accordance with theinstant disclosure.

FIG. 160 depicts utilizing fluoroscopic images to model kinematic motionof the fully assembled knee joint model of FIG. 159.

FIG. 161 includes a depiction of a distal femur bone model and aproximal tibia bone model reflecting real-time tracking of anatomicalaxes in accordance with the instant disclosure.

FIG. 162 includes a knee joint model through a range of motion andreconstructing the helical axes.

FIG. 163 includes a knee joint bone model depicting the anatomical axesin the coronal plane.

FIG. 164 is an exemplary illustration of a clinical examination of aknee joint using inertial measurement units to record motion data inaccordance with the instant disclosure.

FIG. 165 is an exemplary illustration of a clinical examination of aknee joint using inertial measurement units to record motion data inaccordance with the instant disclosure.

FIG. 166 is an exemplary illustration of a clinical examination of aknee joint using inertial measurement units to record motion data inaccordance with the instant disclosure.

FIG. 167 is an exemplary illustration of a clinical examination of aknee joint using inertial measurement units to record motion data inaccordance with the instant disclosure.

FIG. 168 is an exemplary illustration of a clinical examination of aknee joint using inertial measurement units to record motion data inaccordance with the instant disclosure.

FIG. 169 is an exemplary illustration of a clinical examination of aknee joint using inertial measurement units to record motion data inaccordance with the instant disclosure.

FIG. 170 is an exemplary illustration of a clinical examination of aknee joint using inertial measurement units to record motion data inaccordance with the instant disclosure.

FIG. 171 is an exemplary illustration of a clinical examination of aknee joint using inertial measurement units to record motion data inaccordance with the instant disclosure.

FIG. 172 is an exemplary illustration of a clinical examination of aknee joint using inertial measurement units to record motion data inaccordance with the instant disclosure.

FIG. 173 includes a series of photographs, a first of which shows apatient donning a pair of inertial measurement unit (IMU) packages, asecond of which shows the relative size of the IMU package to anindividual IMU, and the third of which shows the relative size of anindividual IMU to a U.S. currency quarter.

FIG. 174 is a screenshot of a user interface in accordance with theinstant disclosure that is depicting a proximal tibia model that isdynamically updated based upon input received from an inertialmeasurement unit in order to provide feedback on load distribution whenthe patient's knee joint is taken through a range of motion.

FIG. 175 is a photograph of the rear, lower back of a patient showingseparate inertial measurement units (IMU) placed over the L1 and L5vertebrae for tracking relative motion of each vertebra through a rangeof motion, as well as an ancillary diagram showing that each IMU is ableto output data indicative of motion across three axes.

FIG. 176 comprises a series of photographs showing the patient and IMUsof FIG. 175 while the patient is moving through a range of motion.

FIG. 177 is a graphical depiction representative of a process fordetermining the relative orientation of at least two bodies usinginertial measurement unit data in accordance with the instantdisclosure.

FIG. 178 comprises a pair of plots showing the absolute change inorientation of anatomical axes relevant to the spine (in particular L1,L5) during lateral bending activities of a patient, where the “(A)” dataplot is representative of a health patient, and the “(B)” data plot isrepresentative of a patient exhibiting spinal degeneration.

FIG. 179 comprises a pair of images, one a tope view of a proximaltibia, and the second an elevated perspective view of the proximaltibia, shown with a surgical navigation tool utilized to normalize IMUsto ensure proper orientation and location of a tibial implant component.

FIG. 180 is an elevated perspective view of an exemplary inertialmeasurement unit calibration device in accordance with the instantdisclosure.

FIG. 181 shows local magnetic field maps (isometric, front, and topviews) generated from data output from an inertial measurement unitbefore calibration (top series of three plots resembling an ellipsoid),and local magnetic field maps (isometric, front, and top views)generated from data output from an inertial measurement unit aftercalibration (bottom series of three plots resembling a sphere).

FIG. 182 comprises a series of diagrams showing exemplary locations ofmagnetometers associated with an inertial measurement unit (A), what thedetected magnetic field from the magnetometers should reflect ifnormalized to account for distortion(s) (B), and the result of a localdistortion in the magnetic field upon the magnetometers if nonormalization is carried out.

FIG. 183 is a series of projections for varied surgical tools eachhaving a unique top surface in order to allow an inertial measurementunit processor to intelligently identify the surgical tool to which theIMU is mounted.

FIG. 184 is an outline drawings representative of a IMU housing anddepicting the interaction between one of the projections of FIG. 183 anda bottom cavity of the IMU housing.

FIG. 185 is an exemplary process flow diagram for preparing a proximalhumerus and implanting a humeral component as part of a shoulderreplacement procedure by using inertial measurement units in accordancewith the instant disclosure.

FIG. 186 is an exemplary process flow diagram for preparing a scapularsocket and implanting a glenoid cavity cup as part of a shoulderreplacement procedure by using inertial measurement units in accordancewith the instant disclosure.

FIG. 187 is an exemplary process flow diagram for preparing a proximalhumerus and implanting a humeral component as part of a reverse shoulderreplacement procedure by using inertial measurement units in accordancewith the instant disclosure.

FIG. 188 is an exemplary process flow diagram for preparing a scapularsocket and implanting a glenoid ball as part of a reverse shoulderreplacement procedure by using inertial measurement units in accordancewith the instant disclosure.

FIG. 189 is a profile and overhead view of an exemplary UWB and IMUhybrid tracking system as part of a tetrahedron module.

FIG. 190 is an illustration of an exemplary central and peripheralsystem in a hip surgical navigation system. The image on the left showsone of the anchor interrogating the peripheral unit's tags at oneinstance of time, and the image on the right shows a different anchorinterrogating the peripheral unit's tags at the following instance oftime. Each anchors interrogate the tags in the peripheral unit todetermine the translations and orientations relative to the anchors.

FIG. 191 is a diagram of an experimental setup of UWB antennas in ananechoic chamber used to measure the UWB antenna 3-D phase centervariation. A lookup table of phase center biases is tabulated duringthis process and used to mitigate phase center variation during systemoperation.

FIG. 192 is an exemplary plot of measured UWB antenna phase center errorversus angle in vertical and horizontal directions for the Vivaldiantenna (E-cut and H-cut). The measured horizontal and vertical phasecenter variation is used to create a lookup table for all possibleangles of arrival. This lookup table is used to mitigate phase centerbias during system operation.

FIG. 193 is an exemplary block diagram of the hybrid system creatingmultiple tags with a single UWB transceiver.

FIG. 194 is an exemplary block diagram of UWB transmitter in accordancewith the instant disclosure.

FIG. 195 is an exemplary UWB pulse signal shown as a function ofamplitude and time.

FIG. 196 is an exemplary plot of measured receiver clock jitter anddrift over a 23 minute interval caused by the 10 MHz clock at thereceiver. The clock jitter and drift can cause up to 30-40 mm of errorin each measured range difference. This can cause 3-D positioning errorsof 30 mm or more. (TD BA: Error between Tag B and anchor A, TD CA: Errorbetween Tag C and anchor A, TD DA: Error between Tag D and anchor A.).

FIG. 197 is an exemplary diagram showing how to calculate the positionof a tag based upon TDOA.

FIG. 198A is a plot showing X position values for both the UWB andoptical tracking systems for an optical rail experiment where the tag ismoved along the rail. The RMSE in the X dimension is 2.52 mm.

FIG. 198B is a plot showing X position values for both the UWB andoptical tracking systems for an optical rail experiment where the tag ismoved along the rail. The RMSE in the X dimension is 0.93 mm.

FIG. 198C is a plot showing X position values for both the UWB andoptical tracking systems for an optical rail experiment where the tag ismoved along the rail. The RMSE in the X dimension is 1.85 mm.

FIG. 199A is a summary listing of parameters fit to the IEEE 802.15.4achannel model with experimental UWB data taken in the operating roomComparison of pathloss for IEEE 802.15.4a LOS channels.

FIG. 199B is a plot of pathloss versus distance showing the ORenvironment is most similar to residential LOS.

FIG. 200 is a table showing expected signal values of calibratedmagnetometer data in cases of no distortion and introduced ferromagneticdistortion.

FIG. 201 is an exemplary block diagram for the processing and fusionalgorithm of the UWB and IMU systems.

FIG. 202 is an overhead view of a central unit and peripheral unit in anexperimental setup. The central unit remains stationary while theperipheral unit is maneuvered during the experiment.

FIG. 203 is a plot of angles versus data samples reflecting orientationtracking using the UWB and IMU systems.

FIG. 204 is a table depicting orientation tracking between the IMUsystem and the hybrid system in a normal and magnetic distortedenvironment.

FIG. 205 is an exemplary block diagram of preoperative preparation andsurgical planning, and the intraoperative use of the surgical navigationsystem to register patient with the computer.

FIG. 206 is an illustration of using one central unit on the pelvis anda minimum of one peripheral unit to be used on the instrument.

FIG. 207 is an illustration of using one central unit adjacent to theoperating area, a peripheral unit on the pelvis and a minimum of oneperipheral unit to be used on the instruments.

FIG. 208 is an illustration of using one central unit and a peripheralunit to obtain cup geometry of the patient for registration.

FIG. 209 is an illustration of using one central unit and a peripheralunit to perform surgical guidance in the direction and depth ofacetabular reaming.

FIG. 210 is an illustration of attachment of the peripheral unit to theacetabular shell inserter.

FIG. 211 is an illustration of attachment of the peripheral unit to thefemoral broach.

FIG. 212 is an illustration of application in body motion tracking. Thisdevice does not require anchors to be placed outside of the human body.The central units are situation towards the center of the body and actsas multiple anchors, while the peripheral units are attached to eachjoint segments. Each central unit interrogates the spatial informationof other central unit as well as the peripheral units to recreate thehuman body motion.

DETAILED DESCRIPTION

The exemplary embodiments of the present disclosure are described andillustrated below to encompass various aspects of orthopedics includingbone and tissue reconstruction, patient-specific and mass customizedorthopedic implants, gender and ethnic specific orthopedic implants,cutting guides, trauma plates, bone graft cutting and placement guides,and patient-specific instruments. Of course, it will be apparent tothose of ordinary skill in the art that the embodiments discussed beloware exemplary in nature and may be reconfigured without departing fromthe scope and spirit of the present invention. However, for clarity andprecision, the exemplary embodiments as discussed below may includeoptional steps, methods, and features that one of ordinary skill shouldrecognize as not being a requisite to fall within the scope of thepresent invention.

Full Anatomy Reconstruction

Referring to FIGS. 1-8, reconstruction of a deformed anatomy or apartial anatomy is one of the complex problems facing healthcareproviders. Loss of anatomy may be the result of birth conditions,tumors, diseases, personal injuries, or failure of previous surgeries.As part of providing treatment for various ailments, healthcareproviders may find it advantageous to reconstruct an anatomy orconstruct an anatomy to facilitate treatment for various conditions thatmay include, without limitation, broken/shattered bones, bonedegeneration, orthopedic implant revision, joint degeneration, andcustom instrumentation design. For example, prior art hip reconstructionsolution requires mirroring of the healthy patient anatomy which may notbe an accurate reflection of the healthy anatomy due to naturallyoccurring asymmetry, as shown in FIG. 15-19.

The present disclosure provides a system and methods for bone and tissuereconstruction. In order to carry out this reconstruction, the systemand associated methods utilizes anatomical images representative of oneor more persons. These images are processed to create a virtual threedimensional (3D) tissue model or a series of virtual 3D tissue modelsmimicking the proper anatomy in question. Thereafter, the system andassociated methods are utilized to create a mold and/or other devices(e.g., fixation devices, grafting devices, patient-specific implants,patient-specific surgical guides) for use with reconstructive surgery.

As represented in FIG. 1, an overview of the exemplary system flowbegins with receiving input data representative of an anatomy. Thisanatomy may comprise a partial anatomy in the case of tissuedegeneration or tissue absence resulting from genetics, or this anatomymay comprise a deformed anatomy resulting from genetics or environmentalconditions, or this anatomy may comprise a shattered tissue resultingfrom one or more anatomy breaks. Input anatomical data comprises twodimensional (2D) images or three dimensional (3D) surfacerepresentations of the anatomy in question that may, for example, be inthe form of a surface model or point cloud. In circumstances where 2Dimages are utilized, these 2D images are utilized to construct a 3Dvirtual surface representation of the anatomy in question. Those skilledin the art are familiar with utilizing 2D images of anatomy to constructa 3D surface representation. Accordingly, a detailed explanation of thisprocess has been omitted in furtherance of brevity. By way of example,input anatomical data may comprise one or more of X-rays, computedtomography (CT) scans, magnetic resonance images (MRIs), or any otherimaging data from which a 3D surface representation of the tissue inquestion may be generated.

Referring to FIG. 50 and Table I, in the context of X-ray images used toconstruct a virtual 3D bone model, it has been discovered that bonerotation during imaging plays an important role in correctlyconstructing the model. In other words, if one attempts to compile X-rayimages in circumstances where bone rotation has occurred between images,the X-ray images need to be normalized to account for this bonerotation.

By way of example, in the context of a proximal femur, it has beendiscovered that bone rotation of six and fifteen degrees results insignificant changes to the measurements extracted from X-ray images. Byway of example, these measurements include, without limitation, proximalangle, head offset, and intramedullary canal width. As reflected inTable I, for the same femur, that was X-ray imaged at zero degrees(i.e., a starting point established by the initial X-ray), six degreesof rotation, and fifteen degrees of rotation exhibited differencesproximal angle, head offset, and intramedullary canal width as measuredusing pixels, where each pixel size was approximately 0.29 millimeters.In particular, proximal angle increased with increasing rotation, as didhead offset, but the same was not true for intramedullary width. In thisexemplary table, three transverse planes were spaced apart along thelongitudinal axis, where each plane corresponded to a location where thewidth of the intramedullary canal was measured. As reflected in Table I,the widths of the intramedullary canal for the same location changedepending upon the angle of rotation. Consequently, as will be discussedin more detail hereafter, when constructing a 3D virtual model of a boneusing X-rays, one must account for rotational deviation to the extentbone rotation occurs during imaging.

It should be understood, however, that the foregoing is an exemplarydescription of anatomies that may be used with the exemplary system andmethods and, therefore, is in no way intended to limit other anatomiesfrom being used with the present system pursuant to the disclosedmethods. As used herein, tissue includes bone, muscle, ligaments,tendons, and any other definite kind of structural material with aspecific function in a multicellular organism. Consequently, when theexemplary system and methods are discussed in the context of bone, thoseskilled in the art should realize the applicability of the system andmethods to other tissue.

Referring back to FIG. 1, the anatomy data input to the system isdirected to three modules, two of which involve processing of theanatomy data (full bone reconstruction module, patient-specific module),while a third (abnormal database module) catalogues the anatomy data aspart of a database. A first of the processing modules, the full bonereconstruction module, processes the input anatomy data with datareceived from the statistical atlas module to generate a virtual, 3Dmodel of the bone(s) in question. This 3D model is a full, normalreconstruction of the bone(s) in question. A second of the processingmodules, the patient-specific module, processes the input anatomy datawith data received from the full bone reconstruction module to generateone or more molds, fixation systems, graft shaping tools, andrenderings, in addition to one or more final orthopedic implants. Arendering refers to visualization of reconstructed anatomy for feedbackregarding expected surgical outcome. More specifically, thepatient-specific module is adapted to generate fully customized devices,designed to precisely fit patient-specific anatomy, despite severedeviation of the patient's anatomy from normal. Moreover, thepatient-specific module utilizes the virtual 3D reconstructed bone modelfrom the full bone reconstruction module to automatically identifyanatomical regions and features for device design parameters (e.g.,fitting region and/or shape). In this fashion, patient-specific data isused to define design parameters so that the output instrument and anyimplant precisely fits the specific anatomy of the patient. Exemplaryutilizations of the patient-specific module will be discussed in greaterdetail hereafter. In order to understand the functions and processes ofthe system in further detail, the following is an explanation of themodules of the system starting with the statistical atlas module.

As shown in FIGS. 1 and 2, the statistical atlas module logs virtual, 3Dmodels of one or more anatomies (e.g., bones) to capture the inherentanatomical variability in a given population. In exemplary form, theatlas logs mathematical representations of anatomical features of theone or more anatomies represented as a mean representation andvariations about the mean representation. By representing the anatomicalfeatures as mathematical representations, the statistical atlas allowsautomated measurements of anatomies and, as will be discussed in moredetail hereafter, reconstruction of missing anatomies.

In order to extract anatomical variations across a common anatomy, inputanatomy data is compared to a common frame of reference across apopulation, commonly referred to as a template 3D model or anatomical 3Dtemplate model. This template 3D model is visually represented on agraphic display as a 3D model that can be rotated and otherwise visuallymanipulated, but comprises a mathematical representation of anatomicalsurface features/representations for all anatomies across thestatistical atlas for the tissue in question (i.e., for a given bone allproperties of the bone are shared across the population of thestatistical atlas, which is generated from the template 3D model). Thetemplate 3D model can be a combination of multiple anatomicalrepresentations or a single representative instance and may representthe lowest entropy state of the statistical atlas. For each anatomy tobe added to the statistical atlas (i.e., input anatomy data), ananatomical 3D model is created and both the anatomical 3D model and thetemplate 3D model are subjected to a normalization process.

During the normalization process, the anatomical 3D model is normalizedrelative to the scale of the template 3D model. The normalizationprocess may involve scaling one or both of the anatomical 3D model andthe template 3D model to have a common unit scale. After normalizationof the anatomical 3D model and the template 3D model, the normalizedanatomical 3D model and template 3D model are rendered scale invariant,so that shape features can be utilized independent of scale (meaningsize in this case). After normalization is complete, both 3D models areprocessed via a scale space mapping and feature extraction sequence.

Scale space mapping and feature extraction is essentially amulti-resolution feature extraction process. In particular, this processextracts shape-specific features at multiple feature scales. Initially,a plurality of anatomical features is selected, each representingfeatures present at a different scale space. Thereafter, for each scalespace representation of the selected anatomical feature, model specificfeatures are extracted. These extracted features are used to draw outrobust (as to noise) registration parameters between the template 3Dmodel and the anatomical 3D model. Subsequent to this multi-resolutionfeature extraction process, the extracted data is processed via amulti-resolution 3D registration process.

Referring to FIGS. 2-5, the multi-resolution 3D registration processuses the scale space extracted features to carry out an affineregistration calculation between the anatomical 3D model and template 3Dmodel in order to register the two models. In particular, the anatomical3D model and template 3D model are processed via a rigid registrationprocess. As represented in FIG. 5, this rigid registration process isoperative to align the anatomical 3D model and template 3D model toensure both models are in the same space and with no pose singularity.In order to align the 3D models, the centroids associated with eachmodel are aligned. In addition, the principle axes for each 3D model arealigned so that the major direction of both 3D models is the same.Finally, the pose difference between the 3D models is minimized bycarrying out an iterative closest point calculation.

Post rigid registration, the 3D models are registered using a similarityregistration process. This process involves aligning the template 3Dmodel and the anatomical 3D model in normal scale iteratively bycalculating a similarity transform that best aligns the normal scalefeatures (i.e., ridges) for both the template 3D model and theanatomical 3D model. The iterative similarity alignment algorithm is avariant of iterative closest point. Within each iteration rotation,translation and scale are calculated between point pairs untilconvergence. Pair matching or correspondence between the two set ofpoints is evaluated using distance query calculated using Kd-tree, orsome other space partitioning data structure. In particular, the ridgesfor both models are utilized to carry out a calculate matching pointpairs process. In this exemplary description, ridges refers to points ona 3D model where a single principle curvature has extrema along itscurvature lines. As part of the calculate matching point pairs process,points are identified on ridges of the 3D models that match one another.Next, the ridges of both 3D models are subjected to a similaritytransformation calculation process where rotation, translation, andscale are calculated that best align the ridges of both models. Atransform points process follows, which is operative to apply thecalculated rotation, translation, and scale to the template 3D modelridges. Thereafter, the root mean square error or distance error betweeneach matched point set is calculated, followed by calculation of thechange in relative root mean square error or distance error from theprevious process. If the change in relative root mean square error ordistance error is within a predetermined threshold, then atransformation process occurs to apply the final rotation, translation,and scale to the template 3D model.

An articulated registration process follows the similarity registrationprocess and receives input data from a scale space features process. Inthe scale space feature process, feature are extracted from the template3D model and the anatomical 3D model in different scale spaces. Eachscale space is defined by convolving the original anatomical 3D modelwith Gaussian smoothing function.

The purpose of the articulated registration process is to match “n”scale space features of the template 3D model with “m” scale spacefeatures calculated on the anatomical 3D model. The difference betweenthe number of detected features on the template 3D model and theanatomical 3D model is due to anatomical variation. This difference in anumber of detected features may result in many relationships between thetemplate 3D model and the anatomical 3D model. Therefore, a two-way,mutual feature matching is performed to accommodate such variation andachieve accurate matching between all mutual features. Specifically,feature sets are computed on the template 3D model in scale space. Inthis exemplary process, feature sets are connected sets of points thatrepresent a prominent anatomical structure (e.g., acetabular cup in thepelvis, spine process in the lumbar). Likewise, feature sets arecomputed on the anatomical 3D model in scale space. A matching featurepair process matches the feature sets computed on the template 3D modelto the feature sets on the anatomical 3D model using shape descriptors(e.g., curvature, shape index, etc.). The result of this process is an“n-m” mapping of feature sets between the template 3D model and theanatomical 3D model. If necessary, a regrouping process is carried outto regroup the matched feature sets into a single feature set (e.g., ifacetabular cup was detected as two pieces, this process would regroupthe two pieces into one single feature set). Thereafter, a calculationprocess is carried out to calculate the correspondence between eachpoint in matched feature sets on the template 3D model and theanatomical 3D model. An affine calculation transformation processfollows in order to calculate the rotation, translation, and shear thattransform each matched feature set on the template 3D model to itscorresponding feature set on the anatomical 3D model. Thereafter, thetemplate 3D model is transformed using the calculated affinetransformation parameters (i.e., rotation, translation, and shear).Finally, a rigid alignment process is carried out to align each matchedfeature set on the template 3D model and the anatomical 3D model.

A non-rigid registration process, occurring after the articulatedregistration process and the normal scale features process, involvesmatching all surface vertices on the template 3D model to vertices onthe anatomical 3D model and calculating initial correspondence. Thiscorrespondence is then used to calculate deformation fields that moveeach vertex on the template 3D model to the matched point on theanatomical 3D model. Matching is done between vertices within the sameclass (i.e., scale space feature vertex; normal scale feature vertex, ornon-feature vertex). In the context of the normal scale featuresprocess, shape features are calculated on the template 3D model and theanatomical 3D model in the original scale space (ridges), meaning theoriginal input model.

Specifically, as part of the non-rigid registration process, the scalespace features are calculated on the template 3D model (TMssf) and onthe anatomical 3D model (NMssf). Each set of features on the template 3Dmodel and on the anatomical 3D model are grown using “k” neighborpoints. An alignment process is applied to the template 3D model scalespace features to match its corresponding feature on the anatomical 3Dmodel. Given two point clouds, reference (X) and moving (Y), the goal isto iteratively align the two point clouds to minimize overall errormetric, under constraint of a minimum relative root mean squared errorand maximum angle threshold. A realignment process is carried out toalign feature sets on the template 3D model with the matching sets onthe anatomical 3D model using iterative closest point in normal scale.Post realignment, the point correspondence between points in eachfeature set on the template 3D model with the matched feature set on theanatomical 3D model is calculated. The matched point on the anatomical3D model should have a surface normal direction close to the template 3Dmodel point. The output is forwarded to the calculate deformation fieldsstep.

Parallel to the scale space features calculation course, template 3Dmodel (TMnfp) and anatomical 3D model (NMnfp) non-feature points or theremaining set of points on the template 3D model surface that does notbelong to either scale space features or normal scale features areprocessed pursuant to a correspondence calculation to calculate thepoint correspondence between non-feature points on the template 3D modeland non-feature points on the anatomical 3D model. The matched point(s)on the new model should have a surface normal direction close to thetemplate model point. The output is forwarded to the calculatedeformation fields step.

Also parallel to the scale space features calculation course, normalscale features (i.e., ridges) on the template 3D model (TM nsf) arealigned with the normal scale features (i.e., ridges) on the anatomical3D model (NM nsf) using AICP. AICP is a variant of the iterative closestpoint calculation where in each iteration translation, rotation, andscale are calculated between matched point sets. After the alignmentprocess, a correspondence process is carried out.

The outputs from scale space features calculation course, thecorrespondence course, and the alignment course are subjected to adeformation process where the deformation field is calculated to moveeach point on the template 3D model to its matched point on theanatomical 3D model.

The output of the non-rigid registration process is a subjected to arelaxation process in order to move the vertices of the template 3Dmodel mesh closer to surface of the anatomical 3D model after themulti-resolution registration step and smooth the output model. Inparticular, the template 3D model in normal space (TM ns) and theanatomical 3D model in normal space (NM ns) are processed via acorrespondence calculation to compute the closest vertices on template3D model to the anatomical 3D model using a normal constrained sphericalsearch algorithm. This calculation, using the closest vertices for bothmodels, generates a correspondence vector from each vertex in thetemplate 3D model and its matched vertices in anatomical 3D model, whichmay result in more than one match point from the anatomical 3D model.Using the matched points for each vertex on the template 3D model, theweighted mean of the matched points on the anatomical 3D model iscalculated based on the Euclidian distance from the point and matchedpoints. At this point, the template 3D model is updated using theweighted average so as to move each point on template 3D model using thecalculated weighted average distance. After the computed weightsprocess, a relaxation process is carried out for every point on templatemodel in order to find the closest point on the anatomical 3D modelsurface and move it to that point. Finally, a smoothing operation isperformed on the deformed template 3D model to remove noise. Theresultant registered 3D models (i.e., template and anatomical 3D models)are then subjected to a free form deformation process.

The free form deformation process morphs the surface of the template 3Dmodel with the surface of the anatomical 3D model. More specifically,the surface of the template 3D model is iteratively moved on a weightedpoint-to-point basis using mutually matched points on both the template3D model surface and the anatomical 3D model surface.

Referencing FIGS. 2 and 6, after the free form deformation process, theanatomical 3D model is subjected to a correspondence calculation processto determine the deviation between the anatomical 3D model and themorphed template 3D model. This correspondence calculation processrefines the template 3D model from the free form deformation step toperform a final match of the selected landmark locations on the templatedeformed 3D model and the deformed anatomical 3D model. In this fashion,the correspondence calculation process calculates and records thevariation in size and shape between the 3D models, which is recorded asdeviation about the mean model. The output of this correspondencecalculation process is the addition of a normalized anatomical 3D modeland a revised template 3D model having been updated to account for thevariations in the anatomical 3D model. In other words, the output of theprocess outlined in FIG. 2 is the normalized anatomical 3D model havingbeen modified to have properties (e.g., point correspondence) consistentwith the revised template 3D model to facilitate full anatomicalreconstruction (e.g., full bone reconstruction).

Referring to FIGS. 1 and 7, inputs from the statistical atlas module andanatomy data are directed to a full anatomy reconstruction module. Byway of example, the anatomy in question may be a bone or multiple bones.It should be noted, however, that anatomies other than bone may bereconstructed using the exemplary hardware, processes, and techniquesdescribed herein. In exemplary form, the full anatomy reconstructionmodule may receive input data as to a partial, deformed, or shatteredpelvis. Input anatomical data comprises two dimensional (2D) images orthree dimensional (3D) surface representations of the anatomy inquestion that may, for example, be in the form of a surface model orpoint cloud. In circumstances where 2D images are utilized, these 2Dimages are utilized to construct a 3D surface representation of theanatomy in question. Those skilled in the art are familiar withutilizing 2D images of anatomy to construct a 3D surface representation.Accordingly, a detailed explanation of this process has been omitted infurtherance of brevity. By way of example, input anatomical data maycomprise one or more of X-rays, computed tomography (CT) scans, magneticresonance images (MRIs), or any other imaging data from which a 3Dsurface representation may be generated. As will be discussed in moredetail hereafter, this input anatomical data may be used, withoutlimitation, for: (1) a starting point for identifying the closeststatistical atlas 3D bone model; (2) registration using a set of 3Dsurface vertices; and, (3) a final relaxation step of reconstructionoutput.

As depicted in FIG. 7, the input anatomical data (e.g., bone model ofthe patient) is utilized to identify the anatomical model (e.g., bonemodel) in the statistical atlas that most closely resembles the anatomyof the patient in question. This step is depicted in FIG. 3 as findingthe closest bone in the atlas. In order to initially identify a bonemodel in the statistical atlas that most closely resembles the patient'sbone model, the patient's bone model is compared to the bone models inthe statistical atlas using one or more similarity metrics. The resultof the initial similarity metric(s) is the selection of a bone modelfrom the statistical atlas that is used as an “initial guess” for asubsequent registration step. The registration step registers thepatient bone model with the selected atlas bone model (i.e., the initialguess bone model) so that the output is a patient bone model that isaligned with the atlas bone model. Subsequent to the registration step,the shape parameters for aligned “initial guess” are optimized so thatthe shape matches the patient bone shape.

Shape parameters, in this case from the statistical atlas, are optimizedso that the region of non-deformed or existing bone is used to minimizethe error between the reconstruction and patient bone model. Changingshape parameter values allows for representation of different anatomicalshapes. This process is repeated, at different scale spaces, untilconvergence of the reconstructed shape is achieved (possibly measured asrelative surface change between iterations or as a maximum number ofallowed iterations).

A relaxation step is performed to morph the optimized tissue to bestmatch the original patient 3D tissue model. Consistent with theexemplary case, the missing anatomy from the reconstructed pelvis modelthat is output from the convergence step is applied to thepatient-specific 3D pelvis model, thereby creating a patient-specific 3Dmodel of the patient's reconstructed pelvis. More specifically, surfacepoints on the reconstructed pelvis model are relaxed (i.e., morphed)directly onto the patient-specific 3D pelvis model to best match thereconstructed shape to the patient-specific shape. The output of thisstep is a fully reconstructed, patient-specific 3D tissue modelrepresenting what should be the normal/complete anatomy of the patient.

Referencing FIG. 1, the abnormal database is utilized as a data inputand training for the defect classification module. In particular, theabnormal database contains data specific to an abnormal anatomicalfeature that includes an anatomical surface representation and relatedclinical and demographic data.

Referencing FIGS. 1 and 8, the fully reconstructed, patient-specific 3Dtissue model representing the normal/complete tissue and inputanatomical data (i.e., 3D surface representation or data from which a 3Dsurface representation may be generated) representingabnormal/incomplete tissue from the abnormal database are input to thedefect classification module. This anatomical data from the abnormaldatabase may be a partial anatomy in the case of tissue degeneration ortissue absence resulting from genetics, or this anatomy may be adeformed anatomy resulting from genetics or environmental conditions(e.g., surgical revisions, diseases, etc.), or this anatomy may be ashattered tissue resulting from one or more anatomy breaks. By way ofexample, input anatomical data may comprise one or more of X-rays,computed tomography (CT) scans, magnetic resonance images (Mills), orany other imaging data from which a 3D surface representation may begenerated.

The defect classification module pulls a plurality of abnormal 3Dsurface representations from abnormal database coupled with the normal3D representation of the anatomy in question to create a quantitativedefect classification system. This defect classification system is usedto create “templates” of each defect class or cluster. More generally,the defect classification module classifies the anatomical deficiencyinto classes which consist of closely related deficiencies (referring tothose with similar shape, clinical, appearance, or othercharacteristics) to facilitate the generation of healthcare solutionswhich address these deficiencies. The instant defect classificationmodule uses software and hardware to classify the defects automaticallyas a means to eliminate or reduce discrepancies between pre-operativedata and intra-operative observer visualization. Traditionally,pre-operative radiographs have been taken as a means to qualitativelyanalyze the extent of anatomical reconstruction necessary, but thisresulted in pre-operative planning that was hit-or-miss at best.Currently, intra-operative observers make the final determination of theextent of anatomy deficiency and many times conclude that thepre-operative planning relying on radiographs was defective orincomplete. As a result, the instant defect classification moduleimproves upon current classification systems by reducing interobserverand intraobserver variation related to defect classification andproviding quantitative metrics for classifying new defect instances.

As part of the defect classification module, the module may take as aninput one or more classification types to be used as an initial state.For example, in the context of a pelvis, the defect classificationmodule may use as input defect features corresponding to the AmericanAcademy of Orthopedic Surgeons (AAOS) D'Antonio et al. bone defectclassification structure. This structure includes four different classesas follows: (1) Type I, corresponding to segmental bone loss; (2) TypeII, corresponding to cavitary bone loss; (3) Type III, corresponding tocombined segmental and cavitary bone loss; and, (4) Type IV,corresponding to pelvis discontinuity. Alternatively, the defectclassification module may be programmed with the Paprosky bone defectclassification structure, depicted graphically for the pelvis in FIG.10. This structure includes three different classes as follows: (1) TypeI, corresponding to supportive rim with no bone lysis; (2) Type II,corresponding to distorted hemispheres with intact supportive columnsand less than two centimeters of superomedial or lateral migration; and,(3) Type III, corresponding to superior migration greater than twocentimeters and sever ischial lysis with Kohler's line broken or intact.Moreover, the defect classification module may be programmed with theModified Paprosky bone defect classification structure. This structureincludes six different classes as follows: (1) Type 1, corresponding tosupportive rim with no component migration; (2) Type 2A, correspondingto distorted hemisphere but superior migration less than threecentimeters; (3) Type 2B, corresponding to greater hemisphere distortionhaving less than ⅓ rim circumference and the dome remaining supportive;(4) Type 2C, corresponding to an intact rim, migration medial toKohler's line, and the dome remains supportive; (5) Type 3A,corresponding to superior migration, greater than three centimeters andsevere ischial lysis with intact Kohler's line; and, (6) Type 3B,corresponding to superior migration, greater than three centimeters andsevere ischial lysis with broken Kohler's line and rim defect greaterthan half the circumference. Using the output classification types andparameters, the defect classification module compares the anatomicaldata to that of the reconstructed data to discern which of theclassification types the anatomical data most closely resembles, therebycorresponding to the resulting assigned classification.

As an initial step, the add to statistical atlas step involvesgenerating correspondence between normal atlas 3D bone model and theabnormal 3D bone model. More specifically, the 3D bone models arecompared to discern what bone in the normal 3D model is not present inthe abnormal 3D model. In exemplary form, the missing/abnormal bone isidentified by comparing points on the surface of each 3D bone model andgenerating a list of the discrete points on the surface of the normal 3Dbone model that are not present on the abnormal 3D bone model. Thesystem may also record and list (i.e., identify) those surface points incommon between the two models or summarily note that unless recorded aspoints being absent on the abnormal 3D bone model, all other points arepresent in common in both bone models (i.e., on both the normal andabnormal bone models). Accordingly, the output of this step is theabnormal 3D bone model with statistical atlas correspondence and a listof features (points) from the normal atlas 3D bone model indicating ifthat feature (point) is present or missing in the abnormal 3D bonemodel.

After generating correspondence between the normal atlas 3D bone model(generated from the full bone reconstruction module) and the abnormal 3Dbone model (generated from the input anatomical data), themissing/abnormal regions from the abnormal 3D bone model are localizedon the normal atlas 3D bone model. In other words, the normal atlas 3Dbone model is compared to the abnormal 3D bone model to identify andrecord bone missing from the abnormal 3D bone model that is present inthe normal atlas 3D bone model. Localization may be carried out in amultitude of fashions including, without limitation, curvaturecomparison, surface area comparisons, and point cloud area comparisons.Ultimately, in exemplary form, the missing/abnormal bone is localized asa set of bounding points identifying the geometrical bounds of themissing/abnormal region(s).

Using the bounding points, the defect classification module extractsfeatures from the missing/abnormal region(s) using input clinical data.In exemplary form, the extracted features may include shape information,volumetric information, or any other information used to describe theoverall characteristics of the defective (i.e., missing or abnormal)area. These features may be refined based on existing clinical data,such as on-going defect classification data or patient clinicalinformation not necessarily related to the anatomical feature(demographics, disease history, etc.). The output of this step is amathematical descriptor representative of the defective area(s) that areused in a subsequent step to group similar tissue (e.g., bone)deformities.

The mathematical descriptor is clustered or grouped based upon astatistical analysis. In particular, the descriptor is statisticallyanalyzed and compared to other descriptors from other patients/cadaversto identify unique defect classes within a given population. Obviously,this classification is premised upon multiple descriptors from multiplepatients/cadavers that refine the classifications and identifications ofdiscrete groups as the number of patients/cadavers grows. The outputfrom this statistical analysis is a set of defect classes that are usedto classify new input anatomical data and determines the number oftemplates.

The output of the defect classification module is directed to a templatemodule. In exemplary form, the template module includes data that isspecific as to each of the defect classifications identified by thedefect classification module. By way of example, each template for agiven defect classification includes surface representations of thedefective bone, location(s) of the defect(s), and measurements relatingto the defective bone. This template data may be in the form of surfaceshape data, point cloud representations, one or more curvature profiles,dimensional data, and physical quantity data. Outputs from the templatemodule and the statistical atlas are utilized by a mass customizationmodule to design, test, and allow fabrication of mass customizedimplants, fixation devices, instruments or molds. Exemplary utilizationsof the mass customization module will be discussed in greater detailhereafter.

Patient-Specific Reconstruction Implants

Referring to FIGS. 1 and 20, an exemplary process and system aredescribed for generating patient-specific orthopedic implant guides andassociated patient-specific orthopedic implants for patients afflictedwith partial, deformed, and/or shattered anatomies. For purposes of theexemplary discussion, a total hip arthroplasty procedure will bedescribed for a patient with a partial anatomy. It should be understood,however, that the exemplary process and system are applicable to anyorthopedic implant amenable to patient-specific customization ininstances where incomplete or deformed anatomy is present. For example,the exemplary process and system are applicable to shoulder replacementsand knee replacements where bone degeneration (partial anatomy), bonedeformation, or shattered bones are present. Consequently, though a hipimplant is discussed hereafter, those skilled in the art will understandthe applicability of the system and process to other orthopedicimplants, guides, tools, etc. for use with original orthopedic ororthopedic revision surgeries.

Pelvis discontinuity is a distinct form of bone loss most oftenassociated with total hip arthroplasty (THA), in which osteolysis oracetabular fractures can cause the superior aspect of the pelvis tobecome separated from the inferior portion. The amount and severity ofbone loss and the potential for biological in-growth of the implant aresome of the factors that can affect the choice of treatment for aparticular patient. In the case of severe bone loss and loss of pelvicintegrity, a custom tri-flange cup may be used. First introduced in1992, this implant has several advantages over existing cages. It canprovide stability to pelvic discontinuity, eliminate the need forstructural grafting and intraoperative contouring of cages, and promoteosseointegration of the construct to the surrounding bone.

Regardless of the context, whether partial, deformed, and/or shatteredanatomies of the patient are at issue, the exemplary system and processfor generating patient-specific implants and/or guides utilizes theforegoing exemplary process and system of 3D bone model reconstruction(see FIGS. 1-7 and the foregoing exemplary discussion of the same) togenerate a three dimensional model of the patient's reconstructedanatomy. More specifically, in the context of total hip arthroplastywhere pelvis discontinuity is involved, the exemplary patient-specificsystem utilizes the patient pelvis data to generate a 3D model of thepatient's complete pelvis, which is side specific (right or left).Consequently, a discussion of the system and process for utilizingpatient anatomy data for a partial anatomy and generating a 3Dreconstructed model of the patient's anatomy is omitted in furtheranceof brevity. Accordingly, a description of the process and system forgenerating patient-specific orthopedic implant guides and associatedpatient-specific orthopedic implants for patients afflicted withpartial, deformed, and/or shattered anatomies will be described postformation of the three dimensional reconstructed model.

Referring specifically to FIGS. 20-22 and 27, after the patient-specificreconstructed 3D bone model of the pelvis and femur are generated, boththe incomplete patient-specific 3D bone model (for pelvis and femur) andthe reconstructed 3D bone model (for pelvis and femur) are utilized tocreate the patient-specific orthopedic implant and a patient-specificplacement guide for the implant and/or its fasteners. In particular, theextract defect shape step includes generating correspondence between thepatient-specific 3D model and the reconstructed 3D model (correspondencebetween pelvis models, and correspondence between femur models, but notbetween one femur model and a pelvis model). More specifically, the 3Dmodels are compared to discern what bone in the reconstructed 3D modelis not present in the patient-specific 3D model. In exemplary form, themissing/abnormal bone is identified by comparing points on the surfaceof each 3D model and generating a list of the discrete points on thesurface of the reconstructed 3D model that are not present on thepatient-specific 3D model. The system may also record and list (i.e.,identify) those surface points in common between the two models orsummarily note that unless recorded as points being absent on thepatient-specific 3D model, all other points are present in common inboth models (i.e., on both the reconstructed and patient-specific 3Dmodels).

Referring to FIG. 21, after generating correspondence between thereconstructed 3D model (generated from the full bone reconstructionmodule) and the patient-specific 3D model (generated from the inputanatomical data), the missing/abnormal regions from the patient-specific3D model are localized on the reconstructed 3D model. In other words,the reconstructed 3D model is compared to the patient-specific 3D modelto identify and record bone missing from the patient-specific 3D modelthat is present in the reconstructed 3D model. Localization may becarried out in a multitude of fashions including, without limitation,curvature comparison, surface area comparisons, and point cloud areacomparisons.

Ultimately, in exemplary form, the missing/abnormal bone is localizedand the output comprises two lists: (a) a first list identifyingvertices corresponding to bone of the reconstructed 3D model that isabsent or deformed in the patient-specific 3D model; and, (b) a secondlist identifying vertices corresponding to bone of the reconstructed 3Dmodel that is also present and normal in the patient-specific 3D model.

Referencing FIGS. 21, 22, and 27, following the extract defect shapestep, an implant loci step is performed. The two vertices lists from theextract defect shape step and a 3D model of a normal bone (e.g., pelvis,femur, etc.) from the statistical atlas (see FIGS. 1 and 2, as well asthe foregoing exemplary discussion of the same) are input to discern thefixation locations for a femoral or pelvic implant. More specifically,the fixation locations (i.e., implant loci) are automatically selectedso that each is positioned where a patient has residual bone.Conversely, the fixation locations are not selected in defect areas ofthe patient's residual bone. In this manner, the fixation locations arechosen independent of the ultimate implant design/shape. The selectionof fixation locations may be automated using shape information andstatistical atlas locations.

As show in FIG. 21, after the implant loci step, the next step is togenerate patient-specific implant parameters. In order to complete thisstep, an implant parameterized template is input that defines theimplant by a set number of parameters that are sufficient to define theunderlying shape of the implant. By way of example, in the case of apelvis reconstruction to replace/augment an absent or degenerativeacetabulum, the implant parameterized template includes angle parametersfor the orientation of the replacement acetabular cup and depthparameters to accommodate for dimensions of the femoral head. Otherparameters for an acetabular implant may include, without limitation,the acetabular cup diameter, face orientation, flange locations andshapes, location and orientation of fixation screws. In the case ofporous implants, the location and structural characteristics of theporosity should be included. By way of example, in the case of a femoralreconstruction to replace/augment an absent or degenerative femur, theimplant parameterized template includes angle parameters for theorientation of the replacement femoral head, neck length, head offset,proximal angle, and cross-sectional analysis of the exterior femur andintercondylar channel. Those skilled in the art will understand that theparameters chosen to define the underlying shape of the implant willvary depending upon the anatomy being replaced or supplemented.Consequently, an exhaustive listing of parameters that are sufficient todefine the underlying shape of an implant is impractical. Nevertheless,as depicted in FIG. 22 for example, the reconstructed 3D pelvis modelmay be utilized to obtain the radius of the acetabular cup,identification of pelvic bone comprising the acetabular cupcircumferential upper ridge, and identification of the orientation ofthe acetabular cup with respect to the residual pelvis. Moreover, theparameters may be refined taking into account the implant loci so thatthe implant best/better fits the patient-specific anatomy.

Subsequent to finalizing the set number of parameters that aresufficient to define the underlying shape of the implant, the design ofthe implant is undertaken. More specifically, an initial iteration ofthe overall implant surface model is constructed. This initial iterationof the overall implant surface model is defined by a combination ofpatient-specific contours and estimated contours for the implantedregion. The estimated contours are determined from the reconstructed 3Dbone model, missing anatomical bone, and features extracted from thereconstructed 3D bone model. These features and the location of theimplant site, which can be automatically determined, are used todetermine the overall implant shape, as depicted for example in FIG. 22for an acetabular cup implant.

Referring back to FIG. 20, the initial iteration of the overall implantsurface model is processed pursuant to a custom (i.e., patient-specific)planning sequence. This custom planning sequence may involve inputs froma surgeon and an engineer as part of an iterative review and designprocess. In particular, the surgeon and/or engineer may view the overallimplant surface model and the reconstructed 3D bone model to determineif changes are needed to the overall implant surface model. This reviewmay result in iterations of the overall implant surface model untilagreement is reached between the engineer and surgeon. The output fromthis step is the surface model for the final implant, which may be inthe form of CAD files, CNC machine encoding, or rapid manufacturinginstructions to create the final implant or a tangible model.

Referring to FIGS. 20, 22, and 23, contemporaneous with or after thedesign of the patient-specific orthopedic implant is the design of apatient specific placement guide. In the context of an acetabular cupimplant, as discussed in exemplary form above, one or more surgicalinstruments can be designed and fabricated to assist in placing thepatient-specific acetabular cup. Having designed the patient-specificimplant to have a size and shape to match that of the residual bone, thecontours and shape of the patient-specific implant may be utilized andincorporated as part of the placement guide.

In exemplary form, the acetabular placement guide comprises threeflanges that are configured to contact the ilium, ischium, and pubissurfaces, where the three flanges are interconnected via a ring.Moreover, the flanges of the placement guide may take on the identicalshape, size, and contour of the acetabular cup implant so that theplacement guide will take on the identical position as planned for theacetabular cup implant. In other words, the acetabular placement guideis shaped as the negative imprint of the patient anatomy (ilium,ischium, and pubis partial surfaces), just as the acetabular cup implantis, so that the placement guide fits on the patient anatomy exactly. Butthe implant guide differs from the implant significantly in that itincludes one or more fixation holes configured to guide drilling forholes and/or placement of fasteners. In exemplary form, the placementguide includes holes sized and oriented, based on image analysis (e.g.,microCT), to ensure proper orientation of any drill bit or other guide(e.g., a dowel) that will be utilized when securing the acetabular cupimplant to the residual pelvis. The number of holes and orientationvaries depending upon the residual bone, which impacts the shaped of theacetabular cup implant too. FIG. 23 depicts an example of apatient-specific placement guide for use in a total hip arthroplastyprocedure. In another instance, the guide can be made so that it fitsinto the implant and guides only the direction of the fixation screws.In this form, the guide is shaped as the negative of the implant, sothat it can be placed directly over the implant. Nevertheless, theincorporation of at least part of the patient-specific reconstructedimplant size, shape, and contour is a theme that carries over regardlessof the intended bone to which the patient-specific implant will becoupled.

Utilizing the exemplary system and method described herein can provide awealth of information that can result in higher orthopedic placementaccuracy, better anatomical integration, and the ability topre-operatively measure true angles and plane orientation via thereconstructed three dimensional model.

Creation of Customized Implants Using Massively Customizable Components

Referring to FIG. 26, an exemplary process and system are described forgenerating customized orthopedic implants using massively customizablecomponents. For purposes of the exemplary discussion, a total hiparthroplasty procedure will be described for a patient with severeacetabular defects. It should be understood, however, that the exemplaryprocess and system are applicable to any orthopedic implant amenable tomass customization in instances where incomplete anatomy is present.

Severe acetabular defects require specialized procedures and implantcomponents to repair. One approach is the custom triflange, which afully custom implant consisting of an acetabular cup and three flangesthat are attached to the ilium, ischium, and pubis. In contrast to theexemplary process and system, prior art triflange implants comprise asingle complex component, which is cumbersome to manufacture andrequires that the entire implant be redesigned for every case (i.e.,completely patient-specific). The exemplary process and system generatesa custom triflange implant that makes use of massively customizablecomponents in addition to fully custom components in a modular way toallow custom fitting and porosity.

A preplanning step in accordance with the exemplary process is performedto determine the orientation of the three flanges relative to the cup,the flange contact locations, and the acetabular cup orientation andsize. This preplanning step is conducted in accordance with the“Patient-specific Implants” discussion immediately preceding thissection. By way of example, specific locations of implant fixation aredetermined pursuant to an implant loci step and using its prefatory datainputs as discussed in the immediately preceding section. By way ofrecall, as part of this implant loci step, the two vertices lists fromthe extract defect shape step and a 3D model of a normal pelvis from thestatistical atlas (see FIGS. 1 and 2, as well as the foregoing exemplarydiscussion of the same) are input to discern the fixation locations forthe custom triflange. More specifically, the fixation locations (i.e.,implant loci) are selected so that each is positioned where a patienthas residual bone. In other words, the fixation locations are notselected in defect areas of the patient's residual pelvis. In thismanner, the fixation locations are chosen independent of the ultimateimplant design/shape.

After determining the fixation locations, the triflange components(i.e., flanges) are designed using the “Patient-specific Implants”discussion immediately preceding this section. The flanges are designedto be oriented relative to the replacement acetabular cup so that thecup orientation provides acceptable joint functionality. Additionally,the contact surfaces of the flanges are contoured to match the patient'spelvis anatomy in that the contact surfaces of the triflanges are shapedas a “negative” of the pelvis's bony surface. The exemplary process ofFIG. 23 utilizes the final step of the process depicted in FIG. 17 torapid prototype the flanges (or use conventional computer numericalcontrol (CNC) equipment). After the flanges are fabricated, furthermachining or steps may be performed to provide cavities within whichporous material may be added to the triflanges.

One portion of the triflange system that does not need to be a customcomponent is the acetabular cup component. In this exemplary process, afamily of acetabular cups is initially manufactured and provides thefoundation on which to build the triflange system. These “blank” cupsare retained in inventory for use as needed. If a particular porosityfor the cup is desired, mechanical features are added to the cup thatallows press fitting of porous material into the cup. Alternatively, ifa particular porosity for the cup is desired, the cup may be coatedusing one or more porous coatings.

After the blank cup is formed and any porosity issues are addressed asdiscussed above, the cup is rendered patient-specific by machining thecup to accept the flanges. In particular, using the virtual model of theflanges, the system software constructs virtual locking mechanisms forthe flanges, which are transformed into machine coding so that thelocking mechanisms are machined into the cup. These locking mechanismsallow the cup to be fastened to the flanges so that when the flanges aremounted to the patient's residual bone, the cup is properly orientedwith respect to the residual pelvis. This machining may use conventionalCNC) equipment to form the locking mechanisms into the blank cups.

Subsequent to fabrication of the locking mechanisms as part of the blankcup, the flanges are mounted to the cup using the interface between thelocking mechanisms. The triflange assembly (i.e., final implant) issubjected to an annealing process to promote strong bonding between thecomponents. Post annealing of the triflange implant, a sterilizationprocess occurs followed by appropriate packaging to ensure a sterileenvironment for the triflange implant.

Creation of Mass Customized Implants

Referring to FIG. 28, an exemplary process and system are described forgenerating mass customized orthopedic implant guides and associated masscustomized orthopedic implants for patients afflicted with partial,deformed, and/or shattered anatomies. For purposes of the exemplarydiscussion, a total hip arthroplasty procedure will be described for apatient needing primary joint replacement. It should be understood,however, that the exemplary process and system are applicable to anyorthopedic implant and guides amenable to mass customization ininstances where incomplete anatomy is present. For example, theexemplary process and system are applicable to shoulder replacements andknee replacements where bone degeneration (partial anatomy), bonedeformation, or shattered bones are present. Consequently, though a hipimplant is discussed hereafter, those skilled in the art will understandthe applicability of the system and process to other orthopedicimplants, guides, tools, etc. for use with primary orthopedic ororthopedic revision surgeries.

The exemplary process utilizes input data from a macro perspective and amicro perspective. In particular, the macro perspective involvesdetermination of the overall geometric shape of the orthopedic implantand corresponding anatomy. Conversely, the micro perspective involvesaccounting for the shape and structure of cancellous bone and itsporosity.

The macro perspective includes a database communicating with astatistical atlas module that logs virtual, 3D models of one or moreanatomies (e.g., bones) to capture the inherent anatomical variabilityin a given population. In exemplary form, the atlas logs mathematicalrepresentations of anatomical features of the one or more anatomiesrepresented as a mean representation and variations about the meanrepresentation for a given anatomical population. Reference is had toFIG. 2 and the foregoing discussion of the statistical atlas and how oneadds anatomy to the statistical atlas of a given population. Outputsfrom the statistical atlas are directed to an automatic landmarkingmodule and to a surface/shape analysis module.

The automatic landmarking module utilizes inputs from the statisticalatlas (e.g., regions likely to contain a specific landmark) and localgeometrical analyses to calculate anatomical landmarks for each instanceof anatomy within the statistical atlas. This calculation is specific toeach landmark. The approximate shape of the region is known, forexample, and the location of the landmark being searched for is knownrelative to the local shape characteristics. For example, locating themedial epicondylar point of the distal femur is accomplished by refiningthe search based on the approximate location of medial epicondylarpoints within the statistical atlas. Accordingly, it is known that themedial epicondylar point is the most medial point within this searchwindow, so a search for the most medial point is performed as to eachbone model within the medial epicondylar region defined in thestatistical atlas, with the output of the search being identified as themedial epicondylar point landmark. After the anatomical landmarks areautomatically calculated for each virtual, 3D model within thestatistical atlas population, the virtual, 3D models of the statisticalatlas are directed to a feature extraction module, along withshape/surface analysis outputs.

The shape/surface outputs come from a shape/surface module alsoreceiving inputs from the statistical atlas. In the context of theshape/surface module, the virtual, 3D models within the statisticalatlas population are analyzed for shape/surface features that are notencompassed by the automatic landmarking. In other words, featurescorresponding to the overall 3D shape of the anatomy, but not belongingto features defined in the previous automatic landmarking step arecalculated as well. For example, curvature data is calculated for thevirtual 3D models.

Outputs from the surface/shape analysis module and the automaticlandmarking module are directed to a feature extraction module. Using acombination of landmarks and shape features, mathematical descriptors(i.e. curvature, dimensions) relevant to implant design are calculatedfor each instance in the atlas. These descriptors are used as input to aclustering process.

The mathematical descriptor is clustered or grouped based upon astatistical analysis. In particular, the descriptor is statisticallyanalyzed and compared to other descriptors from the remaining anatomypopulation to identify groups (of anatomies) having similar featureswithin the population. Obviously, this clustering is premised uponmultiple descriptors from multiple anatomies across the population. Asnew instances are presented to the clustering, which were not present inthe initial clustering, the output clusters are refined to betterrepresent the new population. The output from this statistical analysisis a finite number of implants (including implant families and sizes)covering all or the vast majority of the anatomical population.

For each cluster, a parameterization module extracts the mathematicaldescriptors within the cluster. The mathematical descriptors form theparameters (e.g., CAD design parameters) for the eventual implant model.The extracted mathematical descriptors are fed into an implant surfacegeneration module. This module is responsible for converting themathematical descriptors into surface descriptors to generate a 3D,virtual model of the anatomy for each cluster. The 3D, virtual modelcomplements the micro perspective prior to stress testing and implantmanufacturing.

On the micro perspective, for each anatomy of a given population, datais obtained indicative of structural integrity. In exemplary form, thisdata for a bone may comprise microCT data providing structuralinformation as to the cancellous bone. More specifically, the microCTdata may comprise images of the bone in question (multiple microCTimages for multiple bones across a population). These images arethereafter segmented via the extract trabecular bone structure module inorder to extract the three dimensional geometry of the cancellous bonesand create virtual, 3D models for each bone within the population. Theresulting 3D virtual models are input to a pore size and shape module.As depicted graphically in FIG. 84, the 3D virtual models include poroussize and shape information, which is evaluated by the pore size andshape module to determine pore size and size for the cancellous bone.This evaluation is useful to analyze the porous size and shape of thebone within the intramedullary canal so that the stem of the femoralimplant can be treated with a coating or otherwise processed to exhibita porous exterior to promote integration between the residual bone ofthe femur and the femoral implant. The output from this module, incombination with the 3D virtual model output from the implant surfacegeneration module, is directed to a virtual stress testing module.

The stress testing module combines implant porosity data from the poresize and shape module and implant shape data from the implant surfacegeneration module to define the final implant shape model andproperties. For example, the shape and properties include providing aporous coating for the final implant model that roughly matches thecancellous bone porosity for the bone in question. Once the shape andproperties are incorporated, the final implant model undergoes virtualstress testing (finite-element and mechanical analysis) to verify thefunctional quality of the model. To the extent the functional quality isunacceptable, the parameters defining the implant shape and porosity aremodified until acceptable performance is achieved. Presuming the finalimplant model satisfies the stress testing criteria, the final implantmodel is utilized to generate machine instructions necessary to convertthe virtual model into a tangible implant (that may be further refinedby manufacturing processes known to those skilled in the art). Inexemplary form, the machine instructions may include rapid manufacturingmachine instructions to fabricate the final implant through a rapidprototyping process (to properly capture porous structure) or acombination of traditional manufacturing and rapid prototyping.

Creation of Gender/Ethnic Specific Hip Implants

Referring to FIGS. 29-84, an exemplary process and system are describedfor generating gender and/or ethnic specific implants. For purposes ofthe exemplary discussion, a total hip arthroplasty procedure will bedescribed for a patient with requiring primary joint replacement. Itshould be understood, however, that the exemplary process and system areapplicable to any orthopedic implant amenable to customization. Forexample, the exemplary process and system are applicable to shoulderreplacements and knee replacements and other primary joint replacementprocedures. Consequently, though a hip implant is discussed hereafter,those skilled in the art will understand the applicability of the systemand process to other orthopedic implants, guides, tools, etc. for usewith original orthopedic or orthopedic revision surgeries.

The hip joint is composed of the head of the femur and the acetabulum ofthe pelvis. The hip joint anatomy makes it one of the most stable jointsin the body. The stability is provided by a rigid ball and socketconfiguration. The femoral head is almost spherical in its articularportion that forms two-thirds of a sphere. Data has shown that thediameter of the femoral head is smaller for females than males. In thenormal hip, the center of the femoral head is assumed to coincideexactly with the center of the acetabulum and this assumption is used asthe basis for the design of most hip systems. However, the nativeacetabulum is not deep enough to cover all of the native femoral head.The almost rounded part of the femoral head is spheroidal rather thanspherical because the uppermost part is flattened slightly. Thisspheroidal shape causes the load to be distributed in a ring-likepattern around the superior pole.

The geometrical center of the femoral head is traversed by three axes ofthe joint: the horizontal axis; the vertical axis; and, theanterior/posterior axis. The femoral head is supported by the neck ofthe femur, which joints the shaft. The axis of the femoral neck isobliquely set and runs superiorly medially and anteriorly. The angle ofthe inclination of the femoral neck to the shaft in the frontal plane isthe neck shaft angle. In most adults, this angle varies between 90 to135 degrees and is important because it determines the effectiveness ofthe hip abductors, the length of the limb, and the forces imposed on thehip joint.

An angle of inclination greater than 125 degrees is called coxa valga,whereas an angle of inclination less than 125 degrees is called coxavara. Angles of inclination greater than 125 degrees coincide withlengthened limbs, reduced effectiveness of the hip abductors, increasedload on the femoral head, and increased stress on the femoral neck. In acase of coxa vara, angles of inclination less than 125 degrees coincidewith shortened the limbs, increased effectiveness of the hip abductors,decreased load on the femoral head, and decreased stress on the femoralneck. The femoral neck forms an acute angle with the transverse axis ofthe femoral condyles. This angle faces medially and anteriorly and iscalled angle of anteversion. In adult humans, this angle averagesapproximately 7.5 degrees.

The acetabulum lies on the lateral aspect of the hip where the ilium,ischium, and pubis meet. These three separate bones join into theformation of the acetabulum, with the ilium and ischium contributingapproximately two-fifths each and the pubis one-fifth of the acetabulum.The acetabulum is not a deep enough socket to cover all of the femoralhead and has both articulating and non-articulating portions. However,the acetabular labrum deepens the socket to increase stability. Togetherwith labrum, the acetabulum covers slightly more than 50% of the femoralhead. Only the sides of the acetabulum are lined with articularcartilage, which is interrupted inferiorly by the deep acetabular notch.The center part of the acetabular cavity is deeper than the articularcartilage and is nonarticular. This center part is called the acetabularfossae and is separated from the interface of the pelvic bone by a thinplate. The acetabular fossae is a region unique for every patient and isused in creating patient-specific guide for reaming and placement of theacetabular cup component. Additionally, variation of anatomical featuresfurther warrant the need for population specific implant designs.

Some of the problems associated with prior art use of cementlesscomponents can be attributed to the wide variation in size, shape, andorientation of the femoral canal. One of the challenges to orthopedicimplant design of the femoral stem is large variation in themediolateral and anteroposterior dimensions. There is also significantvariation in the ratio of the proximal to distal canal size. Thedifferent combination of various arcs, taper angles, curves, and offsetsin the normal population is staggering. However, that is not the onlyproblem.

Ancestral differences in femora morphology and a lack of definitestandards for modern populations makes designing the proper hip implantsystem problematic. For example, significant differences in anteriorcurvature, torsion, and cross-sectional shape exist between AmericanIndians, American blacks, and American whites. Differences between Asianand Western populations in the femora are found in the anterior bow ofthe femora, where Chinese are more anteriorly bowed and externallyrotated with smaller intramedullary canals and smaller distal condylesthan Caucasian femora. Likewise, Caucasian femora are larger thanJapanese femora in terms of length distal condyle dimensions. Ethnicdifferences also exist in the proximal femur mineral bone density (BMD)and hip axis length between American blacks and whites. The combinedeffects of higher BMD, shorter hip axis length, and shorterintertrochanteric width may explain the lower prevalence of osteoporoticfractures in American black women compared to their white counterparts.Similarly, elderly Asian and American black men were found to havethicker cortices and higher BMD than white and Hispanic men, which maycontribute to greater bone strength in these ethnic groups. In general,American blacks have thicker bone cortices, narrower endostealdiameters, and greater BMD than American whites.

Combining the femur and the pelvic ancestral (and ethnic) differencesbecomes even more challenging to primary hip systems. Revision surgerycreates more complexity. Added to these normal anatomic and ethnicvariations, the difficulties faced by the surgeon who performs revisionoperation are compounded by: (a) distortion of the femoral canal causedby bone loss around the originally placed prostheses; and, (b)iatrogenic defects produced by the removal of the components and cement.

All of the foregoing factors have led a number of hip surgeons to lookfor ways to improve design of uncemented femoral prostheses. In totalhip replacement (primary or revision), the ideal is to establish anoptimal fit between the femoral ball and acetabular cup. The femoralstem neck should have a cruciform cross section to reduce stiffness. Thestem length should be such that the stem has parallel contact with thewalls of the femur over two to three internal canal diameters. Theproximal one third of the stem is porous coated or hydroxylapatite (HA)coated. The stem is cylindrical (i.e. not tapered) to control bendingloads and to allow transmission of all rotational and axial loadsproximally. The femoral head position should reproduce the patient's ownhead center, unless it is abnormal.

One way to attempt to satisfy these goals is to manufacture femoralprostheses individually for each patient. In other words, make aprosthesis that is specific to a particular patient rather than tryingto reshape the patient's bone to fit a readymade prosthesis.

There are some common design rules for patient-specific (or masscustomization) primary and revision hip replacements. Among these designrules are: (1) the hip stem should be collarless (except in revision) toallow uniform distribution of load to the femur; (2) the hip stem shouldhave a modified rhomboidal cross section to maximize fit/fill, butshould maintain rotational stability; (3) the hip stem should be bowedwhen necessary to conform to patient's bone; (4) the hip stem should beinserted along a curved path, with no gaps between the prosthesis andthe bone; (5) the hip stem neck should have cruciform cross section toreduce stiffness; (6) the hip stem length should be such that the stemhas parallel contact with the walls of the femur over two to threeinternal canal diameters; (7) the proximal one third of the hip stem isporous coated or hydroxylapatite (HA) coated; (8) the hip stem iscylindrical (i.e. not tapered) to control bending loads and to allowtransmission of all rotational and axial loads proximally; (9) thefemoral head position of the hip stem should reproduce the patient's ownhead center, unless it is abnormal.

The following is an exemplary process and system for generating masscustomized orthopedic implant for patients needing primary jointreplacement taking into account the gender and/or ethnicity of thepatient population. For purposes of the exemplary discussion, a totalhip arthroplasty procedure will be described for a patient with apartial anatomy. It should be understood, however, that the exemplaryprocess and system are applicable to any orthopedic implant amenable tomass customization in instances where incomplete anatomy is present. Forexample, the exemplary process and system are applicable to shoulderreplacements and knee replacements where bone degeneration (partialanatomy), bone deformation, or shattered bones are present.Consequently, though a femoral component of a hip implant is discussedhereafter, those skilled in the art will understand the applicability ofthe system and process to other orthopedic implants, guides, tools, etc.for use with original orthopedic or orthopedic revision surgeries.

Referring to FIG. 29, an overall process flow is depicted for using astatistical atlas for generation of both mass customized andpatient-specific hip implants. Initially, the process includes thestatistical atlas including several instances of one or more bones beinganalyzed. In the exemplary context of a hip implant, the statisticalatlas includes several instances of bone models for the pelvis bone andthe femur bone. An articulating surface geometry analysis is conductedat least for the acetabular component (i.e., acetabulum) and theproximal femoral component (i.e., femoral head). In particular, thearticulating surface geometry analysis involves calculation oflandmarks, measurements, and shape features on each bone from a givenpopulation of the statistical atlas. In addition, the articulatingsurface geometry analysis includes generating quantitative values, suchas statistics, representative of the calculations. From thesecalculations, a distribution of the calculations is plotted and parsedbased the distribution. For a bell-shaped distribution, for example, itmay be observed that approximately ninety percent (90%) of thepopulation is grouped so that a non-patient-specific implant (e.g., amass customized implant) may be designed and adequately fit thisgrouping, thereby reducing the costs for patients compared withpatient-specific implants. For the remaining ten percent (10%) of thepopulation, a patient-specific implant may be a better approach.

In the context of a mass customized implant, the statistical atlas maybe utilized to quantitatively assess how many different groups (i.e.,different implants) are able to encompass the overwhelming majority of agiven population. These quantitative assessments may result in clustersof data indicating the general parameters for a basic implant designthat, while not patient-specific, would be more specific than anoff-the-shelf alternative.

In the context of a patient-specific implant, the statistical atlas maybe utilized to quantitatively assess what a normal bone embodies anddifferences between the patient's bone and a normal bone. Morespecifically, the statistical atlas may include curvature data that isassociated with a mean or template bone model. This template bone modelcan then be used to extrapolate what the form of the patient's correctbone would be and craft the implant and surgical instruments used tocarry out the implant procedure.

FIG. 30 graphically summarizes the utilization of a statistical atlas indesigning mass customized and patient-specific hip implants. In thecontext of the implant box, reference is had back to FIGS. 20 and 21 andthe associated discussion for these figures. Similarly, in the contextof the planner box, reference is had back to FIG. 20 and the associateddiscussion of the custom planning interface. Finally, in the context ofthe patient-specific guides box, reference is had back to FIG. 22 andthe associated discussion for this figure.

As depicted in FIG. 31, a flow chart is depicted for an exemplaryprocess that may be utilized to design and fabricate gender and/orethnic specific hip implants. In particular, the process includesutilization of a statistical atlas containing various specimens of aproximal femur (i.e., femur including femoral head) that have beenidentified by associated data as being from either a male or a femaleand the ethnicity of the person from which the bone pertains. Moreover,the statistical atlas module logs virtual, 3D models of one or moreanatomies (e.g., bones) to capture the inherent anatomical variabilityin a given gender and/or ethnic population. In exemplary form, the atlaslogs mathematical representations of anatomical features of the one ormore anatomies represented as a mean representation and variations aboutthe mean representation for a given anatomical population that may havea common gender and/or ethnicity (or grouped to have one of a pluralityof ethnicities for which anatomical commonalties exist). Reference ishad to FIG. 2 and the foregoing discussion of the statistical atlas andhow one adds anatomy to the statistical atlas for a given population.Outputs from the statistical atlas are directed to an automaticlandmarking module and to a surface/shape analysis module.

Referring to FIGS. 31-43, the automatic landmarking module utilizesinputs from the statistical atlas (e.g., regions likely to contain aspecific landmark) and local geometrical analyses to calculateanatomical landmarks for each instance of anatomy within the statisticalatlas. By way of example, various proximal femur landmarks arecalculated for each 3D virtual model of a femur that include, withoutlimitation: (1) femoral head center, which is the center point of afemoral head approximated by a sphere; (2) greater trochanter point,which is the point on the greater trochanter having the minimum distanceto the plane passing through the neck shaft point perpendicular to theanatomical neck center line; (3) osteotomy point, which is the pointfifteen millimeters from the end of the lesser trochanter (approximatelythirty millimeters from the lesser trochanter point); (4) neck shaftpoint, which is the point on the head sphere whose tangential planeencloses the minimum femoral neck cross-sectional area; (5) femur waist,which is the cross-section with the smallest diameter along the femurshaft; (6) intramedullary canal waist, which is the cross-section withthe smallest diameter along the intramedullary canal; (7) femoral neckpivot point, which is the point on the femoral anatomical axis thatforms with the femoral head center and the distal end of the femoralanatomical axis an angle equal to the femoral neck angle; and, (8)lesser trochanter point, which is the point on the lesser trochanterregion that most protrudes outward. By way of further example, variousproximal femur axes are calculated for each 3D virtual model of a femurusing the identified anatomical landmarks that include, withoutlimitation: (a) femoral neck anatomical axis, which is coaxial with aline connecting the femur head center with the femur neck center; (b)femoral neck axis, which is coaxial with a line joining the femur headcenter point and the femoral neck pivot point; and, (c) femoralanatomical axis, which is coaxial with a line connecting two pointslying at a distance twenty-three percent and forty percent of the totalfemur length starting from the proximal end of the femur. By way of yetfurther example, various proximal femur measurements are calculated foreach 3D virtual model of a femur using the identified anatomicallandmarks and axes that include, without limitation: (i) proximal angle,which is the 3D angle between femoral anatomical axis and femoral neckanatomical axis; (ii) head offset, which is the horizontal distancebetween the femoral anatomical axis and the femoral head center; (iii)head height, which is the vertical distance between the lessertrochanter point (referenced previously) and femoral head center; (iv)greater trochantor to head center distance, which is the distancebetween the head center and the greater trochanter point (referencedpreviously); (v) neck length, which is the distance between the headcenter and the neck-pivot point (referenced previously); (vi) the headradius, which is the radius of the sphere fitted to femoral head; (vii)neck diameter, which is the diameter of the circle fitted to the neckcross section at plane normal to femoral neck anatomical axis andpassing through neck center point (referenced previously); (viii)femoral neck anteversion transepicondylar angle, which is the anglebetween the transepicondylar axis and femoral neck axis; (ix) femoralneck anteversion posteriorcondylar angle, which is the angle between theposteriorcondylar axis and femoral neck axis; (x) LPFA, which is theangle between mechanical axis and vector pointing to the greatertrochanter; (xi) calcar index area, which is defined by the equation:(Z−X)/Z, where Z is the femur area at 10 centimeters below the midlesser trochanter point and X is the intramedullary canal area at 10centimeters below the mid lesser trochanter point; (xii) canal calcarratio area, which is the ratio between the intramedullary canal area at3 centimeters below the mid-lesser trochanter level to theintramedullary canal area at 10 centimeters below the mid-lessertrochanter; (xiii) XYR area, which is the ratio between theintramedullary canal area at 3 centimeters below the mid-lessertrochanter to the intramedullary canal area at 10 centimeters below themid-lesser trochanter; (xiv) minor/major axes ratio, which is the ratiobetween the minor axis and major axis of a fitted ellipse to theintramedullary canal cross-section at the narrowest point onintramedullary canal; and, (xv) femur radii to intramedullary canalradii ratio, which is the ratio of circle radii, using circles best fitto the circumference of the outer circumference of the femur andintramedullary canal within a plane normal to the femoral anatomicalaxis (this ratio reflects the thickness of the cortical bone and,accordingly, cortical bone loss in cases of osteoporosis).

Referencing FIGS. 31 and 45-47, using the output from the automaticlandmarking module, parameters for the femoral stem are assessed for agiven population. In particular, regardless of whether the population isgrouped based upon ethnicity, gender, or a combination of the two, themedial contour, neck angle, and head offset are assessed.

In the case of the medial contour, this contour with respect to theintramedullary canal for each femur within the population is generatedby intersecting the intramedullary canal with a plane extending throughthe femoral pivot point and having a normal axis perpendicular to boththe femoral anatomical axis and the neck axis (vectors cross product).After the contours are generated for each femur within the population,the population is subdivided into groups using intramedullary canalsize. When subdivided, the contours may be out of plane, so an alignmentprocess is carried out to align all the contours with respect to acommon plane (e.g., an X-Z plane). The alignment process includesaligning the axis which is normal to both the femoral neck axis andanatomical axis to the Y axis then aligning the anatomical axis to the Zaxis. In this fashion, all contours are translated relative to aspecific point in order for the contours to have a common coordinateframe.

After the contours have a common coordinate frame, the femoral neckpoint is utilized to verify that the points of the contours are inplane. In particular, the femoral neck point is a consistent point thatreflects real anatomy and guarantees the points on the contours are inplane. By verifying the points of the contour are in plane, alignmentvariability between population femurs can be significantly reduced,which facilitates utilization of the contours for head offset andimplant angle design.

Referring to FIG. 48, the statistical atlas may also be useful tointerpolate between normal and osteoporotic bones. When designing andsizing a femoral stem, one of the key considerations is intramedullarycanal dimensions. In instances of normal bone, with respect to thefemur, the intramedullary canal is significantly narrower than comparedto a femur exhibiting osteoporosis. This narrower intramedullary canaldimension is the result, at least in part, of bone thicknesses (measuredtransverse to the dominant axis of the femur) decreasing, whichcorrespondingly results in receding of the interior surface of the femurdelineating the intramedullary channel. In this method, a syntheticpopulation is created by interpolating between healthy and severelyosteoporotic bone thicknesses and generating virtual 3D models havingsaid thicknesses. This dataset thusly contains bones corresponding todifferent stages of osteoporosis. This dataset can now be used as aninput to implant stem design.

In exemplary form, the statistical atlas includes a population ofnormal, non-osteoporotic bones and osteoporotic bones, in this case thebone is a femur. Each of these normal femurs of the atlas is quantifiedand represented as a 3D virtual model, in accordance with the processdescribed herein for adding bones to a statistical atlas. Likewise, eachof the osteoporotic bones of the atlas is quantified and represented asa 3D virtual model, in accordance with the process described herein foradding bones to a statistical atlas. As part of the 3D models for normaland osteoporotic bones, intramedullary canal dimensions are recordedalong the longitudinal length of the femur. Using atlas pointcorrespondence, the intramedullary canal is identified on the atlasbones as spanning a fixed percentage of the overall bone length (say 5%)proximal to the lesser trochanter and a second fixed percentage (say 2%)proximal to the distal cortex point. Additionally, points on theexternal bone surface falling within these proximal and distal boundsare used for determining bone thickness, defined as the distance fromthe external point to the nearest point on the IM canal.

In the context of a proximal femur, FIGS. 51-62 confirm that genderdifferences exist across any ethnic population. As depicted in FIGS. 59and 60, the template 3D model of the statistical atlas for a proximalfemur of a woman exhibits statistical significant measurements whencompared to the template 3D model of a proximal femur for a male. Inparticular, the head offset is approximately 9.3% less for females thanfor males. In current implants head offset increases with stem size,which is acceptable in normal female cases. But a problem arises whenaccounting for head offset in cases of osteoporosis and osteopinia wherethe bone loss leads to increase of intramedullary canal size, whichmeans larger stem size and larger offset. Similarly, the neck diameterand head radius are approximately 11.2% less for females than for males.And the neck length is approximately 9.5% less for females than formales. In addition, the proximal angle is approximately 0.2% less forfemales than for males. Finally, the femoral head height isapproximately 13.3% less for females than for males. Consequently, thegender bone data confirms that simply scaling a generic, femoral implant(i.e., gender neutral) will not account for differences in bonegeometries and, hence, a gender based femoral implant is needed.

Referring to FIGS. 63-68, not only do the dimensions of the proximalfemur widely vary across gender lines, but so too does thecross-sectional shape of the femur along the length of theintramedullary canal. In particular, across a given population within astatistical atlas of male and female femurs, males have intramedullarycanal cross-sections that are closer to circular than females. Morespecifically, females have intramedullary canal cross-sections that are8.98% more eccentric than for males. As will be discussed in more detailhereafter, this gender specific data comprises part of the featureextraction data that is plotted to arrive at clusters from which thenumber and general shape parameters are extracted to arrive at thegender specific femoral implants.

As depicted in FIGS. 72-74, the statistical atlas includes calculationsthat correspond to measurements across a given population of femurs(divided by gender) as to the head center offset in theanterior-posterior (AP) direction. In exemplary form, AP direction wasdetermined by a vector that points anteriorly perpendicular to both themechanical axis and the posterior condylar axis. Offset was measuredbetween the femoral head center and two reference points, with the firstreference point being the midpoint of the anatomical axis, and thesecond reference point being the femur neck pivot point. In summary, APhead height relative to the neck pivot point and anatomical axismidpoint did not exhibit significant differences between male and femalefemurs. Again, this gender specific data comprises part of the featureextraction data that is plotted to arrive at clusters from which thenumber and general shape parameters are extracted to arrive at thegender specific femoral implants.

Referring back to FIGS. 28 and 31, the head center offset,cross-sectional shape data of the intramedullary canal, and medialcontour data for the femurs within the statistical atlas populationcomprise part of the extracted feature data that is plotted to discernthe number of clusters present across a given population (one that isgender specific, a second that is ethnic specific presuming thestatistical atlas includes data as to the ethnicity associated with eachbone) in order to design a gender and/or ethnic specific, masscustomized implant consistent with the flow chart and associateddiscussion for FIG. 28. The identified clusters that are gender and/orethnic specific are utilized to extract the parameters necessary todesign a mass customized femoral implant.

Referring to FIG. 76, an exemplary mass-customized femoral component inaccordance with the instant disclosure is depicted. In particular, themass-customized femoral component comprises four primary elements thatinclude a ball, neck, proximal stem, and distal stem. Each of theprimary elements includes an interchangeable interface to allowinterchangeable balls, necks, and stems with the other interchangeableelements. In this fashion, if a larger femoral ball is needed, only thefemoral ball would be exchanged.

Likewise, if a greater neck offset was desired, the neck element wouldbe exchanged for a different neck element providing the requisiteoffset, while retaining the other three elements if appropriate. In thismanner, the femoral component can, within certain limits, be customizedto fit the patient without necessarily sacrificing the fit or kinematicsthat would otherwise be surrendered by using a one-size-fits-allimplant. Accordingly, all of the femoral elements can be exchanged forother mass customized elements to better suit the patient anatomy.

In this exemplary embodiment, the neck is configured to rotate about theaxis of the proximal stem so that the rotational orientation of the neckwith respect to the proximal stem may be adjusted intraoperatively. Inparticular, preoperative measurements may establish the plannedrotational position of the neck with respect to the proximal stem.Nevertheless, intraoperative considerations such as in-vivo kinematictesting may result in the surgeon changing the pre-operative rotationalorientation to provide improved kinematics or avoidance of a particularimpingement. By way of example, the neck includes a cylindrical studhaving an inset circumferential groove having a textured surface. Thiscylindrical stud is received within an axial cylindrical channel of theproximal stem. In addition to this cylindrical channel, a second channelintersects the cylindrical channel and is shaped to receive a platehaving a semi-circular groove that is also textured and configured toengage the textured surface of the inset circumferential groove. A pairof screws fastened to the proximal stem pushes the plate into engagementwith the cylindrical stud so that eventually, rotational motion of thecylindrical stud with respect to the proximal stem is no longerpossible. Accordingly, when this fixed engagement is reached, the screwsmay be loosened to allow rotational motion between the cylindrical studand the proximal stem, such as would be necessary to make rotationaladjustments intraoperatively.

Engagement between the neck and ball may be conventional, whereasengagement between the proximal stem and the distal stem isunconventional. In particular, the proximal stem includes a distal shankthat is threaded and engaged to be threadably received within a threadedopening extending into the distal stem. Accordingly, the proximal stemis mounted to the distal stem by rotation of the proximal stem withrespect to the distal stem so that the threads of the shank engage thethreads of the distal stem opening. Rotation of the proximal stem withrespect to the distal stem is concluded when the proximal stem abuts thedistal stem. However, if rotational adjustment is necessary between theproximal stem and the distal stem, washers may be utilized to provide aspacer corresponding to the correct rotational adjustment. By way offurther example, if greater rotational adjustment is required, thewasher will be greater in thickness, whereas a thinner washer willprovide correspondingly less rotational adjustment.

Each of the primary elements may be fabricated in predeterminedalternatives that account for size and contour variations within a givengender and/or ethnicity. In this fashion, the alternatives of theprimary elements may be mixed and matched to approximate apatient-specific implant that more closely configures to the anatomy ofthe patient than conventional mass customized femoral components, but ata fraction of the cost and process utilized to generate apatient-specific femoral implant.

FIG. 77 depicts a further alternate exemplary mass-customized femoralcomponent in accordance with the instant disclosure is depicted. Inparticular, the mass-customized femoral component comprises five primaryelements that include a ball, neck, proximal stem, intermediate stem,and distal stem. Each of the primary elements includes aninterchangeable interface to allow interchangeable balls, necks, andstems with the other interchangeable elements. Those skilled in the artwill understand that by increasing the number of elements of themass-customized femoral component, akin to stacking slices of thepatient's natural femur to reproduce this bone, one can increasinglyapproach the fit of a patient-specific implant by using mass-customizedelements.

Similar to the anatomical differences between genders and ethnicitiesfor the proximal femur, FIGS. 78-83 confirm that gender and ethnicdifferences exist across a general pelvis population within astatistical atlas. Referring back to FIG. 28, a series of masscustomized acetabular cup implants are designed and fabricated by usingstatistical atlas data (i.e., pelvis population) grouped based upon atleast one of gender and ethnicity. The grouped atlas data is subjectedto an automatic landmarking process and a surface/shape analysis processto isolate the geometry of the acetabular cup within the population, asdepicted graphically in FIG. 78. In addition, as depicted graphically inFIGS. 82 and 83, the landmarking (for location of acetabular ligament)and contour analysis (for evaluating the contours of the acetabular cup)processes lead to feature extraction, from which the anatomical cupimplant surfaces are ultimately generated, as shown in FIG. 79. Thisanalysis shows that the acetabular cup and femoral head are not composedof a single radius of curvature, but several radii, as shown in FIGS. 80and 81.

Creation of Animal-Specific Implants

Referring to FIG. 85, an exemplary system and methods for designing andfabricating an animal-specific (i.e., patient-specific for an animal)implant and associated instrumentation is similar to the processdepicted and explained previously with respect to FIG. 20, which isincorporated herein. As a prefatory matter, images of the animal anatomyare taken and automatically segmented to yield a virtual 3D bone model.Though graphically depicted as CT scan images, it should be understoodthat other imaging modalities besides CT may be utilized such as,without limitation, MRI, ultrasound, and X-ray. The virtual 3D bonemodel of the affected anatomy is loaded into the statistical atlas, inaccordance with the previous exemplary disclosure. Thereafter, inputsfrom the statistical atlas are utilized to reconstruct the bone(s) andcreate a reconstructed virtual 3D bone model. Bone landmarks arecalculated on the surface of the reconstructed virtual 3D bone model toallow determination of the correct implant size. Geometry of affectedbone is then mapped and converted to parametric form, which is then usedto create an animal-specific implant that mimics the residual anatomicalgeometry. In addition to the animal-specific implant, animal-specificinstrumentation is fabricated and utilized for preparation of theanimal's residual bone and placement of the animal-specific implant.

Referring to FIG. 86, an exemplary system and methods for designing andfabricating a mass customized animal implant is similar to the processdepicted and explained previously with respect to FIG. 28, which isincorporated herein. As a prefatory matter, 3D animal bone models fromthe statistical atlas pertinent to the bone(s) in question are subjectedto an automatic landmarking and surface/shape analysis. The automaticlandmarking process uses information stored in the atlas (e.g., regionslikely to contain a specific landmark) and local geometrical analyses toautomatically calculate anatomical landmarks for each 3D animal bonemodel. For each animal bone in question within the statistical atlas,the shape/surface analysis directly extracts features the surfacegeometry of the 3D virtual animal bone models. Thereafter, each of the3D animal bone models have a feature extraction process carried outthereon that uses a combination of landmarks and shape features tocalculate features relevant to implant design. These features are usedas inputs to a clustering process, where the animal bone population isdivided into groups having similar features using a predeterminedclustering methodology. Each resulting cluster represents thoseinstances used to define the shape and size of a single animal implant.A parameterization process follows for each cluster center (implantsize) in order to extract the parameters for an overall implant model(e.g., computer aided design (CAD) parameters). Thereafter, using theextracted parameters, the overall implant surface and size are generatedfor each cluster. Depending upon the cluster the animal patient fallsinto, the mass-customized implant is selected from the requisite groupand implanted.

Creation of Patient-Specific Cutting Guides

Referring to FIGS. 87-102, an exemplary process and system are describedfor integration of multidimensional medical imaging, computer aideddesign (CAD), and computer graphics features for designingpatient-specific cutting guides. For purposes of exemplary explanationonly, the patient-specific cutting guides are described in the contextof a total hip arthroplasty procedure. Nevertheless, those skilled inthe art will realize that the exemplary process and system areapplicable to any surgical procedure for which cutting guides may beutilized.

As represented in FIG. 87, an overview of the exemplary system flowbegins with receiving input data representative of an anatomy. Inputanatomical data comprises two dimensional (2D) images or threedimensional (3D) surface representations of the anatomy in question thatmay, for example, be in the form of a surface model or point cloud. Incircumstances where 2D images are utilized, these 2D images are utilizedto construct a 3D surface representation of the anatomy in question.Those skilled in the art are familiar with utilizing 2D images ofanatomy to construct a 3D surface representation. Accordingly, adetailed explanation of this process has been omitted in furtherance ofbrevity. By way of example, input anatomical data may comprise one ormore of X-rays (taken from at least two views), computed tomography (CT)scans, magnetic resonance images (MRIs), or any other imaging data fromwhich a 3D surface representation may be generated. In exemplary form,the anatomy comprises a pelvis and a femur.

It should be understood, however, that the following is an exemplarydescription of anatomies that may be used with the exemplary system andin no way is intended to limit other anatomies from being used with thepresent system. As used herein, tissue includes bone, muscle, ligaments,tendons, and any other definite kind of structural material with aspecific function in a multicellular organism. Consequently, when theexemplary system and methods are discussed in the context of bonesinvolved with the hip joint, those skilled in the art will realize theapplicability of the system and methods to other tissue.

The femur and pelvis input anatomy data of the system is directed to oneof two modules depending upon the type of input data. In the case ofX-ray data, the 2D X-ray images are input to a non-rigid module in orderto extract 3d bone contours. If the input data is in the form of CTscans or MM images, these scans/images are directed to an autosegmentation module where the scans/images are automatically segmentedto extract the 3D bone contours (and 3D cartilage contours).

Referring to FIG. 88, the non-rigid module uses the multiple X-rayimages taken from at least two different views are subjected to one ormore pre-processing steps. These steps may include one or more of thefollowing: noise reduction and image enhancement. The resultantpre-processed X-ray images are subjected to a calibration step in orderto register the X-ray images. Preferably, the X-ray images have beentaken in the presence of a fixed position calibration device so that theX-ray images are registered with respect to this fixed positioncalibration device. But when no fixed position calibration device ispresent in the X-ray images, the images may nonetheless be calibratedusing common detected features across multiple images. From thiscalibration process, the output is the position of the anatomy relativeto the imager, which is identified by the “Pose” reference in FIG. 88.

The resultant pre-processed X-ray images are subjected to a featureextraction step. This feature extraction step comprises one or morecomputations of image features utilizing the pre-processed X-ray images.By way of example, these computations may include gradient features,contours, textural components, or any other image derived feature. Inthis exemplary process, the feature extraction step outputs the outlineof the anatomy (e.g., bone shape) as represented by the “Contour”reference in FIG. 88, as well as image features as represented by the“Texture” reference, derived from the X-ray images. Both the outlinedanatomy and image feature data is directed to a non-rigid registrationstep.

The non-rigid registration step registers the outputs from the featureextraction step and the calibration step to a 3D template model of theanatomy in question from a statistical atlas. By way of example, the 3Dtemplate model is generated responsive to non-linear principalcomponents from an anatomical database comprising part of thestatistical atlas. During the non-rigid registration step, the 3Dtemplate model has its shape parameters (non-linear principalcomponents) optimized to match the shape parameters of the X-ray imagesresulting from the pose, contour, and texture data. The output from thenon-rigid registration step is a 3D patient-specific bone model, whichis directed to a virtual templating module, similar to the 3Dpatient-specific bone model output from the auto segmentation module forCT scans or MM images.

Referencing FIG. 91, the auto segmentation process is initialized bytaking the CT scans or MRI images, for example, and carrying out anautomatic segmentation sequence. With specific reference to FIG. 90, theautomatic segmentation sequence includes aligning the scans/images withrespect to a base or starting 3D model of the anatomy in question. Afteralignment of the scans/images to the base 3D model, the scans/images areprocessed via an initial deformation process to calculate the normalvectors, determine locations of the profile points, linearly interpolatethe intensity values, filter the resulting profiles using aSavitsky-Golay filter, generate a gradient of the profiles, weigh theprofiles using a Gaussian weight profile equation, determine the maximumprofiles, and use these maximum profiles to deform the base 3D model.The resulting deformed 3D model is projected onto the template 3D modelfrom a statistical atlas for the anatomy in question. Using theparameters of the template 3D model, the deformed 3D model is furtherdeformed in a secondary deformation process to resemble features uniqueo the template 3D model. After this latter deformation process, thedeformed 3D model is compared to the scans/images to discern whethersignificant differences exist.

In circumstances where significant differences exist between thedeformed 3D model and the scans/images, the deformed 3D model and thescans/images are again subjected to the initial deformation processfollowed by the secondary deformation process. This looping process iscontinued until the deformed 3D model is within a predeterminedtolerance(s) for differences between the deformed 3D model and thescans/images.

After the deformed 3D model has been determined to exhibit less thansignificant differences with respect to the previous iteration or amaximum number of iterations is achieved, the surface edges of thedeformed 3D model as smoothed, followed by a higher resolution remeshingstep to further smooth the surfaces to create a smoothed 3D model. Thissmoothed 3D model is subjected to an initial deformation sequence(identical to the foregoing initial deformation process prior to surfacesmoothing) to generate a 3D segmented bone model.

Referring back to FIG. 91, the 3D segmented bone model is processed togenerate contours. In particular, the intersection of the 3D segmentedbone model and the scans/images are calculated, which result in binarycontours at each image/scan plane.

The 3D segmented bone model is also processed to generate a statistical3D model of the bone appearance that is patient-specific. In particular,the appearance of the bone and any anatomical abnormality is modeledbased on image information present in within the contours and externalto the contours.

The bone contours are thereafter reviewed by a user of the segmentationsystem. This user may be a segmentation expert or infrequent user of thesegmentation system that notices one or more areas of the 3D model thatdo not correlate with the segmented regions. This lack of correlationmay exist in the context of a missing region or a region that is clearlyinaccurate. Upon identification of one or more erroneous regions, theuser may select a “seed point” on the model indicating the center of thearea where the erroneous region exists, or manually outlines the missingregions. The software of the system uses the seed point to add orsubtract from the contour local to the seed point using the initialscans/images of the anatomy from CT or MRI. For example, a user couldselect a region where an osteophyte should be present and the softwarewill compare the scans/images to the region on the 3D model in order toadd the osteophyte to the segmentation sequence. Any changes made to the3D model are ultimately reviewed by the user and verified or undone.This review and revision sequence may be repeated as many times asnecessary to account for anatomical differences between the scans/imagesand the 3D model. When the user is satisfied with the 3D model, theresulting model may be manually manipulated to remove bridges and touchup areas of the model as necessary prior to being output to the virtualtemplating module.

As shown in FIGS. 87 and 92, the virtual templating module receives 3Dpatient-specific models from either or both the auto segmentation moduleand the non-rigid registration module. In the context of a hip joint,the 3D patient-specific models include the pelvis and the femur, whichare both input to an automatic landmarking process. This automaticlandmarking step calculates anatomical landmarks relevant to implantplacement on the femur and pelvis 3D models using regions from similaranatomy present in a statistical atlas and local geometrical searches.

In the context of automatic placement of the femoral stem using distalfixation, as shown in FIG. 93, the automatic landmarking includesdefinition of axes on the femur and the implant. With respect to thefemur, the anatomical femoral axis (AFA) is calculated, followed by theproximal anatomical axis (PAA). The proximal neck angle (PNA) is thencalculated, which is defined as the angle between the AFA and PNA. Withrespect to the femoral implant, the implant axis is along the length ofthe implant stem and the implant neck axis is along the length of theimplant neck. Similar to the PNA of the femur, the implant angle isdefined as the angle between the implant axis and the implant neck axis.The implant is then chosen which has an implant angle that is closest tothe PNA. The implant fitting angle (IFA) is then defined as theintersection of the proximal anatomical axis with a vector drawn fromthe femoral head center at the chosen implant angle.

When using automatic placement of the femoral stem using distal fixationand the calculated anatomical landmarks, as shown in FIG. 93, an implantsizing step determines/estimates for the appropriate implant sizes forfemoral components. The implant size is chosen by comparing the width ofthe implant to the width of the intramedullary canal and selecting theimplant with the most similar width to the intramedullary canal.Thereafter, the system moves forward to an implant placement step.

In the implant placement step for a distal fixation femoral stem, basedon surgeon preferred surgical technique and previously calculatedanatomical landmarks, the initial implant position is determined/chosenfor all relevant implanted components. A resection plane is created tosimulate the proximal femur osteotomy and the implant fit is assessed.Fit assessment is conducted by analyzing the cross sections of thealigned implant and femur intramedullary canal at varying levels alongthe implant axis. The implant is aligned to the femur by aligning theimplant axis to the anatomic femur axis then translating the implant sothat the neck of the implant is in the general location of the proximalfemur neck. The implant is then rotated about the anatomic femur axis toachieve desired anteversion.

As part of this implant placement step, an iterative scheme is utilizedthat includes using an initial “educated guess” as to implant placementas part of a kinematic simulation to evaluate the placement of the“educated guess.” In exemplary form, the kinematic simulation takes theimplant (based upon the placement of the implant chosen) through a rangeof motion using estimated or measured joint kinematics. Consequently,the kinematic simulation may be used to determine impingement locationsand estimate the resulting range of motion of the implant postimplantation. In cases where the kinematic simulation results inunsatisfactory data (e.g., unsatisfactory range of motion,unsatisfactory mimicking of natural kinematics, etc.), another locationfor implant placement may be utilized, followed by a kinematic analysis,to further refine the implant placement until reaching a satisfactoryresult. After the implant position is determined/chosen for all relevantimplanted components, the template data is forwarded to a jig generationmodule.

In the context of automatic placement of the femoral stem using pressfit and three contacts, as shown in FIG. 94, the automatic landmarkingincludes definition of axes on the femur and the implant. With respectto the femur, the anatomical femoral axis (AFA) is calculated, followedby the proximal anatomical axis (PAA). The proximal neck angle (PNA) isthen calculated, which is defined as the angle between the AFA and PNA.With respect to the femoral implant, the implant axis is along thelength of the implant stem and the implant neck axis is along the lengthof the implant neck. Similar to the PNA of the femur, the implant angleis defined as the angle between the implant axis and the implant neckaxis. The implant is then chosen which has an implant angle that isclosest to the PNA. The implant fitting angle (IFA) is then defined asthe intersection of the proximal anatomical axis with a vector drawnfrom the femoral head center at the chosen implant angle.

When using automatic placement of the femoral stem using press fit,three contacts, and the calculated anatomical landmarks, as shown inFIG. 94, an implant sizing step determines/estimates for the appropriateimplant sizes for pelvis and femoral components. The implant size ischosen by aligning the implant to the femur by aligning the implant axisto the anatomic femur axis. The implant is then rotated to align itsneck axis with the femoral neck axis. The implant is then translated tobe in an anatomically proper position within the proximal femur.Thereafter, the system moves forward to an implant placement step.

In the implant placement step for a press fit femoral stem, based onsurgeon preferred surgical technique and previously calculatedanatomical landmarks, the initial implant position is determined/chosenfor all relevant implanted components. A resection plane is created tosimulate the proximal femur osteotomy and the implant fit is assessed.Fit assessment is conducted by analyzing a contour of the implant andfemur intramedullary canal. The contour is created by intersecting theintramedullary canal with a plane normal to both anatomical axis andfemoral neck axis, passing through the point of intersection of theanatomical axis and femur neck axis, producing a contour. When theimplant and intramedullary canal contours are generated, only theimplants with widths less than the intramedullary canal width at thesame location are kept, resulting in many possible correct implantsizes. The group of possible sizes is reduced through two strategiesreducing mean square distance error between the implant and theintramedullary canal. The first strategy minimizes the mean square error(MSE) or other mathematical error metric of the distance between bothmedial and lateral sides of the implant and the intramedullary canal.The second strategy minimizes the MSE of the distance between thelateral side of the implant and the intramedullary canal.

As part of this implant placement step, an iterative scheme is utilizedthat includes using an initial “educated guess” as to implant placementas part of a kinematic simulation to evaluate the placement of the“educated guess.” In exemplary form, the kinematic simulation takes theimplant (based upon the placement of the implant chosen) through a rangeof motion using estimated or measured joint kinematics. Consequently,the kinematic simulation may be used to determine impingement locationsand estimate the resulting range of motion of the implant postimplantation. In cases where the kinematic simulation results inunsatisfactory data (e.g., unsatisfactory range of motion,unsatisfactory mimicking of natural kinematics, etc.), another locationfor implant placement may be utilized, followed by a kinematic analysis,to further refine the implant placement until reaching a satisfactoryresult. After the implant position is determined/chosen for all relevantimplanted components, the template data is forwarded to a jig generationmodule.

Referring back to FIG. 87, the jig generation module generates apatient-specific guide model. More specifically, from the template dataand associated planning parameters, the shape and placement of apatient-specific implant is known with respect to the patient's residualbone. Consequently, the virtual templating module, using thepatient-specific 3D bone model, calculates the position of the implantwith respect to the patient's residual bone and, thus, provides the jiggeneration module with information as to how much of the patient'sresidual bone is intended to be retained. Consistent with this boneretention data, the jig generation module utilizes the bone retentiondata to assign one or more bone cuts to reduce the patient's currentbone to the residual bone necessary to accept the implant as planned.Using the intended bone cut(s), the jig generation module generates avirtual 3D model of a cutting guide/jig having a shape configured tomate with the patient's bone in a single location and orientation. Inother words, the 3D model of the cutting jig is created as a “negative”of the anatomical surface of the patient's residual bone so that thetangible cutting guide precisely matches the patient anatomy. In thisfashion, any guesswork associated with positioning of the cutting jig iseliminated. After the jig generation module generates the virtual 3Dmodel of the cutting jig, the module outputs machine code necessary fora rapid prototyping machine, CNC machine, or similar device to fabricatea tangible cutting guide. By way of example, the exemplary cutting jigfor resection of the femoral head and neck comprises a hollow slot thatforms an associated guide to constrain a cutting blade within a certainrange of motion and maintains the cutting blade at a predeterminedorientation that replicates the virtual cuts from the surgical planningand templating modules. The jig generation module is also utilized tocreate a placement jig for the femoral stem.

Referring to FIG. 100, subsequent to resecting the femoral head andneck, intramedullary reaming followed by femoral stem insertion takesplace. In order to prepare the femur for insertion of the femoralimplant, reaming of the intramedullary canal needs to take place alongan orientation consistent with the orientation of the femoral implant.If the reaming is offset, the orientation of the femoral implant may becompromised. To address this concern, the jig generation modulegenerates a virtual guide that is a “negative” of the anatomical surfaceof the patient's residual or resected bone so that a rapid prototypingmachine, CNC machine, or similar device can fabricate the cutting guidethat precisely matches the patient anatomy. By way of example, thereaming jig may include an axial guide along which the reamer maylongitudinally traverse. Using this reaming jig, the surgeon performingthe reaming operation is ensured of reaming in the proper orientation.

The intramedullary canal may receive the femoral stem. Again, to ensurethe femoral stem is properly positioned both from a rotationalperspective and an angular perspective within the intramedullary canal,the jig generation module generates a femoral stem placement guide. Byway of example, the femoral stem placement guide concurrently is a“negative” of the anatomical surface of the patient's residual orresected bone as well as the top of the femoral stem. In this manner,the placement guide slides over the femoral shaft (portion of femoralstem that the femoral ball is connected to) and concurrently includes aunique shape to interface with the patient's residual or resected boneso that only a single orientation of the femoral stem is possible withrespect to the patient's femur, thereby ensuring proper implantation ofthe femoral stem consistent with pre-operative planning. It should benoted, however, that while the exemplary jigs have been described in thecontext of a primary hip implant, those skilled in the art shouldunderstand that the foregoing exemplary process and system are notlimited to primary hip implants or limited to hip implant or revisionsurgical procedures. Instead, the process and system are applicable toany hip implants in addition to surgical procedures involving otherareas of the body including, without limitation, knee, ankle, shoulder,spine, head, and elbow.

As depicted in FIG. 101, in the context of the acetabulum, the jiggeneration module may generate instructions for fabricating reaming andacetabular implant placement guides for the acetabular cup. Inparticular, from the template data and associated planning parameters,the shape and placement of a patient-specific acetabular implant isknown with respect to the patient's residual pelvis. Consequently, thevirtual templating module, using the patient-specific 3D acetabulummodel, calculates the size and position of the acetabular cup implantwith respect to the patient's residual bone and, thus, provides the jiggeneration module with information as to how much of the patient'sresidual pelvis is intended to be retained and the desired implantorientation. Consistent with this bone retention data, the jiggeneration module utilizes the bone retention data to assign one or morebone cuts/reaming to reduce the patient's current pelvis to the residualbone necessary to accept the acetabular implant as planned. Using theintended bone cut(s), the jig generation module generates a virtual 3Dmodel of a cutting guide/jig having a shape configured to mate with twoportions of the patient's pelvis via only one orientation. In otherwords, the 3D model of the cutting jig is created as a “negative” of theanatomical surface of the patient's pelvis so that the tangible reamingguide precisely matches the patient anatomy. In this fashion, anyguesswork associated with positioning of the reaming jig is eliminated.After the jig generation module generates the virtual 3D model of thereaming jig, the module outputs machine code necessary for a rapidprototyping machine, CNC machine, or similar device to fabricate atangible reaming jig. By way of example, the exemplary acetabularcomponent jig for reaming the acetabulum comprises a four-piecestructure, where a first piece is configured to be received in thenative acetabulum and temporarily mount to the second piece until thesecond piece is secured to the pelvis using the first piece as aplacement guide. After the second piece is fastened to the pelvis, thefirst piece may be removed. Thereafter, the third piece includes acylindrical or partially cylindrical component that uniquely interfaceswith the second piece to ensure the reamer can longitudinally traversewith respect to the third piece, but its orientation is fixed using acombination of the first and third pieces. Following reaming, the reameris removed and the third piece is removed from the first piece. Theacetabular cup implant is mounted to the reamed acetabulum using a forthpiece. In particular, the fourth piece is shaped uniquely to engage thefirst piece in only a single orientation, while at the same time beingformed to be received within the interior of the acetabular cup implant.After the implant cup is positioned, both the first and fourth piecesare removed. It should also be noted that additional jigs may be createdfor drilling one or more holes into the pelvis to seat the acetabularimplant, where each drilling jig is mounted in succession to the firstpiece in order to verify the orientation of the drill bit.

Surgical Navigation

Referring to FIGS. 103-111, an alternate exemplary system and processare depicted for using one or more inertial measurement units (IMUS) tofacilitate surgical navigation to accurately position orthopedicsurgical tools and orthopedic implants during a surgical procedure. Thisfirst alternate exemplary embodiment is described in the context ofperforming a total hip arthroplasty procedure. Nevertheless, themethods, systems, and processes described hereafter are applicable toany other surgical procedure for which guidance of surgical tools andimplants is useful.

As depicted schematically, the initial steps of utilizing patient images(whether X-ray, CT, MRI, etc.) and performing segmentation orregistration to arrive at virtual templates of the patient's anatomy andappropriate implant size, shape, and placement parallels that previouslydescribed with reference to FIGS. 87, 88, 90-92. What differs somewhatare the modules and processes utilized downstream from the virtualtemplating module.

Downstream from the virtual templating module is an initialization modelgeneration module. This module receives template data and associatedplanning parameters (i.e., the shape and placement of a patient-specificacetabular implant is known with respect to the patient's residualpelvis, as well as the shape and placement of a patient-specific femoralimplant with respect to the patient's residual femur). Using thispatient-specific information, the initialization model generation modulefabricates a 3D virtual model of an initialization device for thepatient's native acetabular cup and a 3D virtual model of aninitialization device for the femoral implant. In other words, the 3Dmodel of the acetabular initialization device is created as a “negative”of the anatomical surface of the patient's acetabulum so that thetangible initialization device precisely matches the patient'sacetabulum. Similarly, the 3D model of the femoral stem initializationdevice is created as a “negative” of the anatomical surface of thepatient's residual femur and femoral implant so that the tangibleinitialization device precisely matches the patient's residual femur andfemoral implant at only a single location and single orientation. Inaddition to generating these initialization devices, the initializationmodel generation module also generates machine codes necessary for arapid prototyping machine, CNC machine, or similar device to fabricatethe tangible acetabular initialization device and femoral initializationdevice. The tangible acetabular initialization device and femoralinitialization device are fabricated and mounted to (or formedconcurrently or integrally with) or integral with surgical navigationtools configured to have at least one IMU 1002.

IMUs 1002, capable of reporting orientation and translational data, arecombined with (e.g., mounted to) the surgical tools to assist insurgical navigation, which includes positioning surgical equipment andimplant devices. These IMUs 1002 are communicatively coupled (wired orwireless) to a software system that receives output data from the IMUsindicating relative velocity and time that allows the software tocalculate the IMU's current position and orientation, or the IMU 1002calculates and sends the position and orientation of the surgicalinstrument, which will be discussed in more detail hereafter, theposition and orientation of the surgical instrument associated with theIMU. In this exemplary description, each IMU 1002 includes threegyroscopes, three accelerometers, and three Hall-effect magnetometers(set of three, tri-axial gyroscopes, accelerometers, magnetometers) thatmay be integrated into a single circuit board or comprised of separateboards of one or more sensors (e.g, gyroscope, accelerometer,magnetometer) in order to output data concerning three directionsperpendicular to one another (e.g., X, Y, Z directions). In this manner,each IMU 1002 is operative to generate 21 voltage or numerical outputsfrom the three gyroscopes, three accelerometers, and three Hall-effectmagnetometers. In exemplary form, each IMU 1002 includes a sensor boardand a processing board, with a sensor board including an integratedsensing module consisting of a three accelerometers, three gyroscopicsensors and three magnetometers (LSM9DS, ST-Microelectronics) and twointegrated sensing modules consisting of three accelerometers, and threemagnetometers (LSM303, ST-Microelectronics). In particular, the IMUs1002 each include angular momentum sensors measuring rotational changesin space for at least three axes: pitch (up and down), yaw (left andright) and roll (clockwise or counter-clockwise rotation). Morespecifically, each integrated sensing module consisting magnetometer ispositioned at a different location on the circuit board, with eachmagnetometer assigned to output a voltage proportional to the appliedmagnetic field and also sense polarity direction of a magnetic field ata point in space for each of the three directions within a threedimensional coordinate system. For example, the first magnetometeroutputs voltage proportional to the applied magnetic field and polaritydirection of the magnetic field in the X-direction, Y-direction, andZ-direction at a first location, while the second magnetometer outputsvoltage proportional to the applied magnetic field and polaritydirection of the magnetic field in the X-direction, Y-direction, andZ-direction at a second location, and the third magnetometer outputsvoltage proportional to the applied magnetic field and polaritydirection of the magnetic field in the X-direction, Y-direction, andZ-direction at a third location. By using these three sets ofmagnetometers, the heading orientation of the IMU may be determined inaddition to detection of local magnetic field fluctuation. Eachmagnetometer uses the magnetic field as reference and determines theorientation deviation from magnetic north. But the local magnetic fieldcan, however, be distorted by ferrous or magnetic material, commonlyreferred to as hard and soft iron distortion. Soft iron distortionexamples are materials that have low magnetic permeability, such ascarbon steel, stainless steel, etc. Hard iron distortion is caused bypermanent magnets. These distortions create a non-uniform field (seeFIG. 184), which affects the accuracy of the algorithm used to processthe magnetometer outputs and resolve the heading orientation.Consequently, as discuss in more detail hereafter, a calibrationalgorithm is utilized to calibrate the magnetometers to restoreuniformity in the detected magnetic field. Each IMU 1002 may be poweredby a replaceable or rechargeable energy storage device such as, withoutlimitation, a CR2032 coin cell battery and a 200mAh rechargeable Li ionbattery.

The integrated sensing modules in IMU 1002 may include a configurablesignal conditioning circuit and analog to digital converter (ADC), whichproduces the numerical outputs for the sensors. The IMU 1002 may usesensors with voltage outputs, where an external signal conditioningcircuit, which may be an offset amplifier that is configured tocondition sensor outputs to an input range of a multi-channel 24 bitanalog-to-digital converter (ADC) (ADS1258, Texas Instrument). The IMU1002 further includes an integrated processing module that includes amicrocontroller and a wireless transmitting module (CC2541, TexasInstrument). Alternatively, the IMU 1002 may use separate low powermicrocontroller (MSP430F2274, Texas Instrument) as the processor and acompact wireless transmitting module (A2500R24A, Anaren) forcommunication. The processor may be integrated as part of each IMU 1002or separate from each IMU, but communicatively coupled thereto. Thisprocessor may be Bluetooth compatible and provide for wired or wirelesscommunication with respect to the gyroscopes, accelerometers, andmagnetometers, as well as provide for wired or wireless communicationbetween the processor and a signal receiver.

Each IMU 1002 is communicatively coupled to a signal receiver, whichuses a pre-determined device identification number to process thereceived data from multiple IMUs. The data rate is approximately 100 Hzfor a single IMU and decreases as more IMUs join the shared network. Thesoftware of the signal receiver receives signals from the IMUs 1002 inreal-time and continually calculates the IMU's current position basedupon the received IMU data. Specifically, the acceleration measurementsoutput from the IMU are integrated with respect to time to calculate thecurrent velocity of the IMU in each of the three axes. The calculatedvelocity for each axis is integrated over time to calculate the currentposition. But in order to obtain useful positional data, a frame ofreference must be established, which includes calibrating each IMU.

The present disclosure includes a novel system and method forcalibrating one or more IMUs for use in surgical navigation. Priorpatent references to utilizing IMUs as purported aids in surgicalnavigation have suffered from inoperability for numerous reasons. Amongthese reasons include IMU placement with respect to metallic surgicalinstruments as well as an absence of calibrating the IMUs. Morespecifically, in the context of IMUs incorporating magnetometers, localcalibration of the magnetometers is imperative for operative tracking ofsurgical instruments and related orthopedic components.

Referring to FIG. 182, in accordance with the instant disclosure, anovel calibration tool 1000 is utilized to calibrate one or more IMUs1002 that may incorporate magnetometers. In exemplary form, thecalibration tool 1000 includes a stationary base 1006 within which ishoused a controller 1008, a motor 1012, gearing 1016, a drive shaft1020, and a power supply 1024. The drive shaft 1020 is mounted to aportion of the gearing 1016, as is the motor 1012, so that the motor isoperative to drive the gearing and rotate the drive shaft. Inparticular, the motor 1012 comprises an electric motor having a singledrive shaft to which is mounted a drive gear of the gearing 1016. Thisdrive gear engages a secondary gear, which is mounted to the drive shaft1020, so that rotational motion of the motor 1012 is converted intorotational motion of the drive shaft 1020.

In this exemplary configuration, the stationary base 1006 includes acircular exterior that partially defines a hollow interior thataccommodates the motor 1012, the gearing 1016, the controller 1008, thepower supply 1024, and a portion of the drive shaft 1020. By way ofexample, a central vertical axis extends through the stationary base1006 that is coaxial with a central axis of the drive shaft 1020. Thiscoaxial alignment reduces vibration occurring as a result of rotation ofthe drive shaft 1020 with respect to the stationary base 1006. Rotationof the drive shaft 1020 is operative to rotate an outer stage 1030 withrespect to the stationary base 1006.

In exemplary form, a ring-shaped bearing plate 1034 interposes the topof the stationary base 1006 and the bottom of the outer stage 1030. Boththe stationary base 1006 and the bearing plate 1034 includecorresponding axial openings that allow throughput of a portion of thedrive shaft 1020. An end of the drive shaft 1020 proximate the outerstage 1030 is mounted to a slip ring 1038, which is in turn mounted tothe outer stage. In this fashion, rotation of the drive shaft 1020 withrespect to the stationary base 1006 causes the outer stage 1030 torotate around the central vertical axis. As will be discussed in moredetail hereafter, the IMUS 1002 are calibrated in part by rotating theIMUS around the central vertical axis.

In this exemplary embodiment, the outer stage 1030 includes a blockU-shaped profile with corresponding opposed fork appendages 1042. Eachappendage 1042 is mounted to a roller bearing assembly 1046 thatreceives and is pivotally mounted to a center shaft 1050. Each centershaft 1050 is concurrently mounted to opposing lateral sides of an innerplatform 1054 that sits between the fork appendages 1042. The innerplatform 1054 includes a block U-shaped profile, which fits within thecorresponding opposed fork appendages 1042, that includes a base havinga plurality of upstanding projections 1058. As will be discussed in moredetail hereafter, the upstanding projections 1058 are each configured toengage a corresponding recess associated with each IMU 1002 to fix theposition of the IMU with respect to a portion of the calibration tool1000. Each center shaft 1050 is longitudinally aligned along a centralaxis and is mounted to the inner platform 1054 so that rotation of thecenter shafts corresponds with rotation of the inner platform 1054 withrespect to the outer stage 1030.

In order to rotate the inner platform 1054 with respect to the outerstage 1030, the calibration tool includes a pulley 1060 mounted to oneof the center shafts 1050. In particular, one of the center shafts 1050is longer than the other in order to accommodate mounting of the pulley1060 and corresponding rotation of the pulley by way of a drive belt1064 concurrently engaging an electric motor 1068. In this exemplaryembodiment, an output shaft of the electric motor 1068 is mounted to itsown pulley 1072, which engages the drive belt 1064 to ultimately rotatethe pulley 1060 and correspondingly rotates the inner platform 1054 withrespect to the outer stage 1030 (about the longitudinally alignedcentral axis of the center shafts 1050) when the electric motor ispowered. The electric motor 1068 is mounted to a motor mount 1076extending from an underneath side of the outer stage 1030 below one ofthe fork appendages 1042. As will be discussed in more detail hereafter,the IMUs 1002 are calibrated in part by rotating the inner platform 1054with respect to the outer stage 1030, which thus rotates the IMUs withrespect to the longitudinal central axis, which is perpendicular to thecentral vertical axis. Those skilled in the art should understand that athird rotational axis may be introduced to rotate the IMUs about an axisthat is perpendicular to both the longitudinal central axis and thelongitudinal vertical axis. An exemplary calibration sequence forcalibrating one or more IMUs 1002 using the calibration tool 1000 willhereafter be described.

In exemplary form, the IMUs 1002 are preferably calibrated in closeproximity to the location of ultimate use in surgical navigation. Thismay be within an operating room and, more specifically, adjacent apatient bed upon which the patient will or is lying. Calibration of theIMUs is location specific so that calibration of the IMUs farther awayfrom the location of intended use may result in meaningful variance inthe magnetic fields at the location of calibration and the area of use(i.e., the surgical area). Consequently, it is preferably to calibratethe IMUs 1002 near the area of use.

Using the novel calibration tool 1000, each IMU 1002 is mounted to oneof the upstanding projections 1058 of the inner platform 1054. By way ofexample, each IMU 1002 is mounted to a housing having a shaped peripherydelineating an open bottom. The shaped periphery of the IMU 1002 housingis configured to outline the perimeter of the upstanding projections1058 so that the IMU housing can be snap-fit over a correspondingupstanding projection in order to maintain engagement of the IMU housingand the inner platform 1054 during a calibration sequence. By way ofexample, the IMU housing may have an oblong, triangular, rectangular, orother sided periphery that engages a corresponding upstanding projection1058. By way of exemplary discussion and illustration, the IMU housinghas a rectangular opening delineated by a constant verticalcross-section, which is slightly larger than the rectangularcross-section of the upstanding projection 1058. In exemplary form, thecalibration tool 1000 includes four upstanding projections 1058 to allowfor calibration of four IMUs 1002 simultaneously. But, it should benoted that, more or less than four upstanding projections 1058 may beincluded as part of the inner platform 1054 to provide for calibrationof one or more IMUs at the same time.

The goal of the calibration sequence is to establish zero with respectto the accelerometers (i.e., meaning at a stationary location, theaccelerometers provide data consistent with zero acceleration) and tomap the local magnetic field and to normalize the output of themagnetometers to account for directional variance and the amount ofdistortion of the detected magnetic field. In order to calibrate theaccelerometers of the IMUs 1002, the inner platform 1054 remainsstationary with respect to the outer stage 1030, which also remainsstationary with respect to the stationary base 1006. Multiple readingsare taken from all accelerometers with the inner platform 1054 at afirst fixed, stationary position with respect to the outer stage 1030.Thereafter, the inner stage is moved to a second fixed, stationaryposition with respect to the outer stage 1030 and a second set ofmultiple readings are taken from all accelerometers. The outputs fromthe accelerometers at the multiple, fixed positions are recorded, on anaccelerometer specific basis, and utilized to establish a zeroacceleration reading for the applicable accelerometer. In addition toestablishing zero with respect to the accelerometers, the calibrationsequence also maps the local magnetic field and normalizes the output ofthe magnetometers to account for directional variance and the amount ofdistortion of the detected magnetic field.

In order to map the local magnetic field for each magnetometer(presuming multiple magnetometers for each IMU 1002 positioned indifferent locations), the inner platform 1054 is rotated about thecenter shafts 1050 and about the central axis with respect to the outerstage 1030, in addition to the outer stage 1030 being rotated about thedrive shaft 1020 and about the central vertical axis with respect to thestationary base 1006. Output data from each magnetometer is recordedwhile the inner platform 1054 is rotated about two axes perpendicular toone another. Repositioning of the each magnetometer about the twoperpendicular axes generates a point cloud or map of the threedimensional local magnetic field sensed by each magnetometer. FIGS.______ (calibration FIGS. 1-3) depict an exemplary local magnetic fieldmapped from isometric, front, and top views based upon data receivedfrom a magnetometer while being concurrently rotated in two axes. As isreflected in the local magnetic field map, the local map embodies anellipsoid. This ellipsoid shape is the result of distortions in thelocal magnetic field caused by the presence of ferrous or magneticmaterial, commonly referred to as hard and soft iron distortion. Softiron distortion examples are materials that have low magneticpermeability, such as carbon steel, stainless steel, etc. Hard irondistortion is caused by material such as permanent magnets.

It is presumed that but for distortions in the local magnetic field, thelocal magnetic field map would be spherical. Consequently, thecalibration sequence is operative to collect sufficient data point todescribe the local magnetic field in different orientations by eitherthe calibration tool 1000 or manual manipulation of the IMU. Acalibration algorithm calculates the correction factors to map thedistorted elliptic local magnetic field into a uniform spherical field.

Referencing FIG. 184, the multiple magnetometers positioned in differentlocations with respect to one another as part of an IMU 1002 is used todetect local magnetic after the calibration is complete. Absent anydistortion in the magnetic field, each of the magnetometers shouldprovide data indicative of the exact same direction, such as polarnorth. But distortions in the local magnetic field, such as the presenceof ferrous or magnetic materials (e.g. surgical instruments), causes themagnetometers to provide different data as to the direction of polarnorth. In other words, if the outputs from the magnetometers are notuniform to reflect polar north, a distortion has occurred and the IMU1002 may temporary disable the tracking algorithm from using themagnetometer data. It may also alert the user that distortion has beendetected.

Referring to FIGS. 185 and 186, the exemplary surgical tools thatreceive an IMU 1002 include an electrical switch pattern or grid that isunique for each instrument. More specifically, each surgical toolincludes a projection having a top surface that is predominantly planar,but for one or more cylindrical cavities. In exemplary form, each IMU1002 includes a housing defining a bottom opening that is configured toreceive the surgical tool projection. Within this bottom opening arefour switches that each includes a biased cylindrical button so thatwhen the button is depressed, the switch is closed and sends acorresponding signal to the IMU 1002 processor. Conversely, when thebutton is not depressed, the switch remains open and no correspondingsignal of switch closure is sent to the IMU 1002 processor. In thisfashion, the processor determines which switches are open and whichswitches are closed and uses this information to identify which surgicaltool the IMU 1002 is mounted to.

As part of identifying the surgical tool, zero to four of the switchesmay be depressed depending upon the top surface topography of theprojection. As depicted graphically, a projection of a surgical tool isreceived within the IMU 1002 housing bottom opening so that the topsurface of the projection is pushed adjacent the switches. It should benoted that the projection and bottom opening in the IMU 1002 housing areconfigures so that the projection is received within the bottom openingin only a single rotational orientation, thereby limiting the chance ofmisalignment between the projection and switches that might otherwiselead to a misidentification of the surgical tool.

In particular, as depicted in FIG. 185, the calibration adapter surgicaltool includes a single cylindrical cavity positioned near the frontright corner of the projection (opposite the shaved corner) in order toprovide a unique configuration. Accordingly, when the projection of thecalibration adapter surgical tool is received within the bottom openingof the IMU 1002 housing, only a single switch of the 2&2 grid ofswitches is activated nearest the front right corner of the IMU 1002housing, which tells the IMU 1002 processor that the IMU 1002 is mountedto the calibration adapter surgical tool. In contrast, the patientanatomical mapping (PAM) registration tool adapter surgical toolincludes two cylindrical cavities positioned near the right front andrear corners of the projection, in a second unique configuration.Accordingly, when the projection of the PAM adapter surgical tool isreceived within the bottom opening of the IMU 1002 housing, only twoswitches of the 2&2 grid of switches are activated nearest the rightside of the IMU 1002 housing, which tells the IMU 1002 processor thatthe IMU 1002 is mounted to the PAM adapter surgical tool. Moreover, thereamer adapter surgical tool includes two cylindrical cavitiespositioned near the front of the projection (i.e., adjacent the frontleft and right corners). Accordingly, when the projection of the reameradapter surgical tool is received within the bottom opening of the IMU1002 housing, only two switches of the 2&2 grid of switches areactivated nearest the front of the IMU 1002 housing, which tells the IMU1002 processor that the IMU 1002 is mounted to the reamer adaptersurgical tool. Finally, the impacter adapter surgical tool includesthree cylindrical cavities positioned near the front and rights sides ofthe projection (i.e., adjacent the front left and right corners, andrear right corner). Accordingly, when the projection of the impacteradapter surgical tool is received within the bottom opening of the IMU1002 housing, only three switches of the 2&2 grid of switches areactivated nearest the front and right sides of the IMU 1002 housing,which tells the IMU 1002 processor that the IMU 1002 is mounted to theimpacter adapter surgical tool. Those skilled in the art will understandthe variation that may be provided by providing a plurality of switchesor electrical contacts as part of the IMU 1002 that interface with aplurality of projections, cavities, or electrical contacts associatedwith the surgical tool in order to unambiguously identify the surgicaltool to which the IMU 1002 is mounted.

Identification of the surgical tool to which the IMU 1002 is mounted isimportant for accurate surgical navigation. In particular, the surgicalnavigation system in accordance with the instant disclosure includes asoftware package that has been preloaded with CAD models or surfacemodels of each surgical tool to which the IMU 1002 could possibly bemounted. In so doing, the software package knows the relative dimensionsof each surgical tool such as, without limitation, length in theX-direction, width in the Y-direction, and height in the Z-direction andhow these dimensions change along the length, width, and height of thesurgical tool. Thus, when the IMU 1002 is mounted to the surgical toolin a known location, the location and orientation information (by way ofthe gyroscopes, accelerometers, and magnetometers) from the IMU 1002 canbe translated into location and orientation information for the surgicaltool. Therefore, by tracking the IMU 1002 in 3D space, the softwarepackage is able to track the surgical tool to which the IMU 1002 ismounted in 3D space and relay this location and orientation to a user,such as a surgeon or a surgeon's assistant.

In exemplary form, the software package includes a visual display thatis operative to display each surgical tool as a 3D model. When an IMU1002 is mounted to a surgical tool, the IMU 1002 processor sends data tothe software package that allows the software package to identify whichsurgical tool the IMU 1002 is mounted to. After making thisidentification, the software package displays a 3D model of the surgicaltool that is mounted to the IMU 1002 in an orientation that isconsistent with the orientation information derived from the IMU. Inaddition to providing orientation information by manipulating the 3Dvirtual model of the surgical tool in real-time, the software packagealso provides real-time data about the location of the surgical tool byusing a second, reference IMU 1002 that is mounted to a reference object(i.e., a bone of a patient). But before the software package can providemeaningful location information, the IMUS 1002 (IMU#1 mounted to asurgical tool and IMU#2 mounted to a reference object (i.e., bone)) needto be registered with respect to one another.

In exemplary form in the context of a total hip arthroplasty procedure,as depicted in FIGS. 103-110, registration tools are utilized torecreate the template surgical plan by engaging the patient anatomy in apredetermined orientation. When each utility IMU 1002 is mounted to itsregistration tool (one for the femur, a second for the pelvis), theregistration tool is mounted to the relevant bone in a predeterminedorientation (only one orientation that precisely matches the patientanatomy to “zero” the IMU). In order to carry this registration out, asecond reference IMU is rigidly mounted to the bone in question (one IMUmounted to the pelvis and a second IMU mounted to the femur). In otherwords, one utility IMU is mounted to the acetabular registration toolwhile a second reference IMU is rigidly mounted to the pelvis. In thecontext of the femur, one utility IMU is mounted to the femoralregistration tool while a second reference IMU is rigidly mounted to thefemur. As part of the registration process, the software of the computerutilizes the outputs from both IMU (utility and reference) to calculatethe “zero” location for the utility IMU when the registration tool isfinally stationary and located in its unique location and orientation.Thereafter, the IMU 1002 may be removed from the relevant registrationtool and mounted in a predetermined fashion to surgical tools (reamer,saw, implant placement guide, etc.) to ensure the proper orientation andplacement of the surgical tools. The IMU 1002 may be mounted and removedfrom each surgical tool in succession until the surgical procedure isfinished.

In this exemplary embodiment, the acetabular registration tool includesan elongated shaft having a unique projection shaped to fit within thepatient's acetabular cup in only a single orientation (includingrotational position and angular position). A proximal end of theregistration tool includes an IMU 1002 registration holster to receivethe IMU 1002 so that when the IMU 1002 is locked within the holster, theIMU 1002 is rigidly fixed relative to the registration tool and uniqueprojection. Coincident with the registration tool, a second referenceIMU 1002 is rigidly fixed to the pelvis at a known location. When theunique projection of the registration tool is correctly oriented withinthe patient's acetabular cup (and the IMU 1002 locked within theregistration holster and the IMU 1002 mounted to the pelvis areactivated), the orientation of the IMU 1002 locked to the registrationholster relative to the planned implant cup orientation (which is setwhen the unique projection is received within the acetabular cup in onlya single correct orientation) is known. An operator indicates to thesoftware system that the IMUs are in the correct position and thesoftware records the position of each IMU. The registration tool (withthe IMU 1002 locked in the holster) is removed from the anatomy andthereafter the IMU 1002 is removed from the registration holster inpreparation for mounting the IMU 1002 to surgical tools.

By way of example, the IMU 1002 previously mounted to the acetabularregistration tool is removed from the tool and mounted to a surgicaltool in a known location. In exemplary form, the IMU 1002 (previouslymounted to the acetabular registration tool) is fixed rigidly to a cupreamer with a known orientation relative to the reaming direction sothat the orientation of the cup reamer with respect to the pelvis isknown and dynamically updated via both IMUs (IMU 1002 mounted to the cupreamer and IMU 1002 mounted to pelvis).

The software program provides a graphical user interface for a surgeonthat displays virtual models of the patient's pelvis and a virtual modelof the surgical tool in question, in this case a cup reamer (the virtualmodel of the patient's pelvis having already been completed pursuant tothe virtual templating step, and the virtual model of the cup reamer orother surgical tool having been previously loaded into the system forthe particular cup reamer and other surgical tools that may beutilized), and updates the orientation of the pelvis and surgical toolin real time via the graphical user interface providing position andorientation information to the surgeon. Rather than using a graphicaluser interface, the instant system may include surgical devices havingindicator lights indicating to the surgeon whether the reamer iscorrectly oriented and, if not, what direction(s) the reamer needs to berepositioned to correctly orient the reamer consistent with thepre-operative planning. After resurfacing using the cup reamer iscomplete, the IMU 1002 is removed from the cup reamer and fixed rigidlyto a cup inserter with a known orientation relative to the inserterdirection. The cup inserter is then utilized to place the cup implant,with the IMUs continuing to provide acceleration feedback that thesoftware utilizes to calculate position to provide real time feedback asto the position of the pelvis with respect to the cup inserter. To theextent that holes are drilled into the pelvis before or after cuppositioning, the IMU 1002 previously mounted to the registration toolmay be rigidly fixed to a surgical drill to ensure the correctorientation of the drill with respect to the pelvis. An analogousregistration tool and set of IMUs may be used with the software systemto assist with placement of the femoral stem component.

In one exemplary embodiment, the femoral registration tool includes anelongated shaft having a distal form shaped to fit partially over thepatient's femoral neck in only a single orientation (includingrotational position and angular position). A proximal end of theregistration tool includes an IMU 1002 registration holster to receivethe IMU 1002 so that when the IMU 1002 is locked within the holster, theIMU 1002 is rigidly fixed relative to the registration tool and distalform. Coincident with the registration tool, a second reference IMU 1002is rigidly fixed to the femur at a known location. When the distal formof the registration tool is correctly oriented with respect to thefemoral neck (and the IMU 1002 locked within the registration holsterand the IMU 1002 mounted to the femur are activated), the orientation ofthe IMU 1002 locked to the registration holster relative to the femurorientation (which is set when the distal form is received over thefemoral neck in only a single correct orientation) is known. An operatorindicates to the software system that the IMUs are in the correctposition and the software records the position of each IMU. Theregistration tool (with the IMU 1002 locked in the holster) is removedfrom the anatomy and thereafter the IMU 1002 is removed from theregistration holster in preparation for mounting the IMU 1002 tosurgical tools.

By way of example, the IMU 1002 previously mounted to the femoralregistration tool is removed from the tool and mounted to anothersurgical tool in a known location. In exemplary form, the IMU 1002(previously mounted to the femoral registration tool) is fixed rigidlyto a surgical saw in a known location so that movement of the IMU 1002correspondingly translates into known movement of the surgical saw.Given the other IMU 1002 being fixedly mounted to the femur in a knownlocation, the IMUs work together to provide dynamically updatedinformation to the software system about changes in the position (viaacceleration data) of both the femur and surgical saw.

The software program provides a graphical user interface for a surgeonthat displays virtual models of the patient's femur and a virtual modelof the surgical tool in question, in this case a surgical saw (thevirtual model of the patient's femur having already been completedpursuant to the virtual templating step, and the virtual model of thesurgical saw or other surgical tool having been previously loaded intothe system for the particular surgical saw and other surgical tools thatmay be utilized), and updates the orientation of the femur and surgicaltool in real time via the graphical user interface providing positionand orientation information to the surgeon. Rather than using agraphical user interface, the instant system may include surgicaldevices having indicator lights indicating to the surgeon whether thesurgical saw is correctly oriented and, if not, what direction(s) thesurgical saw needs to be repositioned to correctly orient the surgicalsaw to make the correct bone cuts consistent with the pre-operativeplanning. After making the requisite bone cuts, the IMU 1002 is removedfrom the surgical saw and fixed rigidly to a reamer (to correctly reamthe intramedullary canal) and thereafter mounted to a femoral steminserter with a known orientation relative to the inserter direction.The stem inserter is then utilized to place the femoral stem implantwithin the reamed intramedullary canal, with the IMUs continuing toprovide acceleration feedback that the software utilizes to calculateposition of the femur and stem inserter in real time and display thisposition data to the surgeon via the graphical user interface.

In exemplary form in the context of a total shoulder arthroplastyprocedure, as depicted in FIGS. 187 and 188, registration tools areutilized to recreate the template surgical plan by engaging the patientanatomy in a predetermined orientation. When each utility IMU 1002 ismounted to its registration tool (one for the humerus, a second for thescapula), the registration tool is mounted to the relevant bone in apredetermined orientation (only one orientation that precisely matchesthe patient anatomy to “zero” the IMU). In order to carry thisregistration out, a second reference IMU is rigidly mounted to the bonein question (one IMU mounted to the humerus and a second IMU mounted tothe scapula). In other words, one utility IMU is mounted to the humeralregistration tool while a second reference IMU is rigidly mounted to thehumerus. In the context of the scapula, one utility IMU is mounted tothe scapular registration tool while a second reference IMU is rigidlymounted to the scapula. As part of the registration process, thesoftware of the computer utilizes the outputs from both IMU (utility andreference) to calculate the “zero” location for the utility IMU when theregistration tool is finally stationary and located in its uniquelocation and orientation. Thereafter, the IMU 1002 may be removed fromthe relevant registration tool and mounted in a predetermined fashion tosurgical tools (reamer, saw, implant placement guide, etc.) to ensurethe proper orientation and placement of the surgical tools. The IMU 1002may be mounted and removed from each surgical tool in succession untilthe surgical procedure is finished.

In this exemplary embodiment, as depicted in FIG. 188, the scapularregistration tool includes an elongated shaft having a unique projectionshaped to fit within the patient's glenoid cavity in only a singleorientation (including rotational position and angular position). Aproximal end of the registration tool includes an IMU 1002 registrationholster to receive the IMU 1002 so that when the IMU 1002 is lockedwithin the holster, the IMU 1002 is rigidly fixed relative to theregistration tool and unique projection. Coincident with theregistration tool, a second reference IMU 1002 is rigidly fixed to thescapula at a known location. When the unique projection of theregistration tool is correctly oriented within the patient's glenoidcavity (and the IMU 1002 locked within the registration holster and theIMU 1002 mounted to the scapula are activated), the orientation of theIMU 1002 locked to the registration holster relative to the plannedimplant cup orientation (which is set when the unique projection of theregistration tool is received within the glenoid cavity in only a singlecorrect orientation) is known. An operator indicates to the softwaresystem that the IMUs are in the correct position and the softwarerecords the position of each IMU. The registration tool (with the IMU1002 locked in the holster) is removed from the anatomy and thereafterthe IMU 1002 is removed from the registration holster in preparation formounting the utility IMU 1002 to other surgical tools.

By way of example, the IMU 1002 previously mounted to the scapularregistration tool is removed from the tool and mounted to a surgicaltool in a known location. In exemplary form, the IMU 1002 (previouslymounted to the scapular registration tool) is fixed rigidly to a cupreamer with a known orientation relative to the reaming direction sothat the orientation of the cup reamer with respect to the scapula isknown and dynamically updated via both IMUs (IMU 1002 mounted to the cupreamer and IMU 1002 mounted to pelvis).

The software program provides a graphical user interface for a surgeonthat displays virtual models of the patient's scapula and a virtualmodel of the surgical tool in question, in this case a cup reamer (thevirtual model of the patient's scapula having already been completedpursuant to the virtual templating step, and the virtual model of thecup reamer or other surgical tool having been previously loaded into thesystem for the particular cup reamer and other surgical tools that maybe utilized), and updates the orientation of the scapula and surgicaltool in real time via the graphical user interface providing positionand orientation information to the surgeon. Rather than using agraphical user interface, the instant system may include surgicaldevices having indicator lights indicating to the surgeon whether thereamer is correctly oriented and, if not, what direction(s) the reamerneeds to be repositioned to correctly orient the reamer consistent withthe pre-operative planning. After resurfacing using the cup reamer iscomplete, the utility IMU 1002 is removed from the cup reamer and fixedrigidly to a cup inserter with a known orientation relative to theinserter direction. The cup inserter is then utilized to place the cupimplant, with the IMUs continuing to provide acceleration feedback thatthe software utilizes to calculate position to provide real timefeedback as to the position of the scapula with respect to the cupinserter. To the extent that holes are drilled into the scapula beforeor after cup positioning, the utility IMU 1002 previously mounted to theregistration tool may be rigidly fixed to a surgical drill to ensure thecorrect orientation of the drill with respect to the scapula. Ananalogous registration tool and set of IMUs may be used with thesoftware system to assist with placement of the humeral stem component.

In one exemplary embodiment, the humeral registration tool includes anelongated shaft having a distal form shaped to fit partially over thepatient's humeral neck in only a single orientation (includingrotational position and angular position). A proximal end of theregistration tool includes an IMU 1002 registration holster to receivethe IMU 1002 so that when the IMU 1002 is locked within the holster, theIMU 1002 is rigidly fixed relative to the registration tool and distalform. Coincident with the registration tool, a second reference IMU 1002is rigidly fixed to the humerus at a known location. When theregistration tool is correctly oriented with respect to the humeral neck(and the IMU 1002 locked within the registration holster and thereference IMU 1002 mounted to the humerus are activated), theorientation of the IMU 1002 locked to the registration holster relativeto the humerus orientation (which is set when the distal form isreceived over the humeral neck in only a single correct orientation) isknown. An operator indicates to the software system that the IMUs are inthe correct position, and stationary, and the software records theposition of each IMU to establish the reference orientation of thepre-planned direction. The registration tool (with the IMU 1002 lockedin the holster) is removed from the anatomy and thereafter the utilityIMU 1002 is removed from the registration holster in preparation formounting the IMU 1002 to other surgical tools.

By way of example, the IMU 1002 previously mounted to the humeralregistration tool is removed from the tool and mounted to anothersurgical tool in a known location. In exemplary form, the IMU 1002(previously mounted to the humeral registration tool) is fixed rigidlyto a surgical saw in a known location so that movement of the IMU 1002correspondingly translates into known movement of the surgical saw.Given the reference IMU 1002 being fixedly mounted to the humerus in aknown location, the IMUs work together to provide dynamically updatedinformation to the software system about changes in the position (viaacceleration data) of both the humerus and surgical saw.

The software program provides a graphical user interface for a surgeonthat displays virtual models of the patient's humerus and a virtualmodel of the surgical tool in question, in this case a surgical saw (thevirtual model of the patient's humerus having already been completedpursuant to the virtual templating step, and the virtual model of thesurgical saw or other surgical tool having been previously loaded intothe system for the particular surgical saw and other surgical tools thatmay be utilized), and updates the orientation of the humerus andsurgical tool in real time via the graphical user interface providingposition and orientation information to the surgeon. Rather than using agraphical user interface, the instant system may include surgicaldevices having indicator lights indicating to the surgeon whether thesurgical saw is correctly oriented and, if not, what direction(s) thesurgical saw needs to be repositioned to correctly orient the surgicalsaw to make the correct bone cuts consistent with the pre-operativeplanning. After making the requisite bone cuts, the utility IMU 1002 isremoved from the surgical saw and fixed rigidly to a reamer (tocorrectly ream the humeral canal) and thereafter mounted to a humeralstem inserter with a known orientation relative to the inserterdirection. The stem inserter is then utilized to place the humeral stemimplant within the reamed canal, with the IMUs continuing to provideacceleration feedback that the software utilizes to calculate positionof the humerus and stem inserter in real time and display this positiondata to the surgeon via the graphical user interface.

In exemplary form in the context of a reverse shoulder implantprocedure, as depicted in FIGS. 189 and 190, registration tools areutilized to recreate the template surgical plan by engaging the patientanatomy in a predetermined orientation. When each utility IMU 1002 ismounted to its registration tool (one for the humerus, a second for thescapula), the registration tool is mounted to the relevant bone in apredetermined orientation (only one orientation that precisely matchesthe patient anatomy to “zero” the IMU). In order to carry thisregistration out, a second reference IMU is rigidly mounted to the bonein question (one IMU mounted to the humerus and a second IMU mounted tothe scapula). In other words, one utility IMU is mounted to the humeralregistration tool while a second reference IMU is rigidly mounted to thehumerus. In the context of the scapula, one utility IMU is mounted tothe scapular registration tool while a second reference IMU is rigidlymounted to the scapula. As part of the registration process, thesoftware of the computer utilizes the outputs from both IMU (utility andreference) to calculate the “zero” location for the utility IMU when theregistration tool is finally stationary and located in its uniquelocation and orientation. Thereafter, the IMU 1002 may be removed fromthe relevant registration tool and mounted in a predetermined fashion tosurgical tools (reamer, saw, inserter, drill guide, drill, etc.) toensure the proper orientation and placement of the surgical tools. TheIMU 1002 may be mounted and removed from each surgical tool insuccession until the surgical procedure is finished.

In this exemplary embodiment, as depicted in FIG. 190, the scapularregistration tool includes an elongated shaft having a unique projectionshaped to fit within the patient's glenoid cavity in only a singleorientation (including rotational position and angular position). Aproximal end of the registration tool includes an IMU 1002 registrationholster to receive the IMU 1002 so that when the IMU 1002 is lockedwithin the holster, the IMU 1002 is rigidly fixed relative to theregistration tool and unique projection. Coincident with theregistration tool, a second reference IMU 1002 is rigidly fixed to thescapula at a known location. When the unique projection of theregistration tool is correctly oriented within the patient's glenoidcavity (and the IMU 1002 locked within the registration holster and theIMU 1002 mounted to the scapula are activated), the orientation of theIMU 1002 locked to the registration holster relative to the plannedimplant cup orientation (which is set when the unique projection isreceived within the glenoid cavity in only a single correct orientation)is known. An operator indicates to the software system that the IMUs arein the correct position and the software records the position of eachIMU. The registration tool (with the IMU 1002 locked in the holster) isremoved from the anatomy and thereafter the IMU 1002 is removed from theregistration holster in preparation for mounting the utility IMU 1002 toother surgical tools.

By way of example, the IMU 1002 previously mounted to the scapularregistration tool is removed from the tool and mounted to a surgicaltool in a known location. In exemplary form, the IMU 1002 (previouslymounted to the scapular registration tool) is fixed rigidly to a cupreamer with a known orientation relative to the reaming direction sothat the orientation of the cup reamer with respect to the scapula isknown and dynamically updated via both IMUs (IMU 1002 mounted to the cupreamer and IMU 1002 mounted to pelvis).

The software program provides a graphical user interface for a surgeonthat displays virtual models of the patient's scapula and a virtualmodel of the surgical tool in question, in this case a cup reamer (thevirtual model of the patient's scapula having already been completedpursuant to the virtual templating step, and the virtual model of thecup reamer or other surgical tool having been previously loaded into thesystem for the particular cup reamer and other surgical tools that maybe utilized), and updates the orientation of the scapula and surgicaltool in real time via the graphical user interface providing positionand orientation information to the surgeon. Rather than using agraphical user interface, the instant system may include surgicaldevices having indicator lights indicating to the surgeon whether thereamer is correctly oriented and, if not, what direction(s) the reamerneeds to be repositioned to correctly orient the reamer consistent withthe pre-operative planning. After resurfacing using the cup reamer iscomplete, the utility IMU 1002 is removed from the cup reamer and fixedrigidly to a drill plate with a known orientation and location. Thedrill plate is then utilized to drill holes into the scapula, with theIMUs continuing to provide acceleration feedback that the softwareutilizes to calculate position to provide real time feedback as to theposition of the scapula with respect to the drill plate, followed bypositioning of the glenoid base plate and mounting of the glenoidcomponent ball. Though not required, when drilling holes through thedrill plate, the utility IMU 1002 may be rigidly fixed to a surgicaldrill to ensure the correct orientation of the drill with respect to thedrill plate. An analogous registration tool and set of IMUs may be usedwith the software system to assist with placement of the humeral stemcomponent.

In one exemplary embodiment, the humeral registration tool includes anelongated shaft having a distal form shaped to fit partially over thepatient's humeral neck in only a single orientation (includingrotational position and angular position). A proximal end of theregistration tool includes an IMU 1002 registration holster to receivethe IMU 1002 so that when the IMU 1002 is locked within the holster, theIMU 1002 is rigidly fixed relative to the registration tool and distalform. Coincident with the registration tool, a second reference IMU 1002is rigidly fixed to the humerus at a known location. When theregistration tool is correctly oriented with respect to the humeral neck(and the IMU 1002 locked within the registration holster and thereference IMU 1002 mounted to the humerus are activated), theorientation of the IMU 1002 locked to the registration holster relativeto the humerus orientation (which is set when the distal form isreceived over the humeral neck in only a single correct orientation) isknown. An operator indicates to the software system that the IMUs are inthe correct position, and stationary, and the software records theposition of each IMU to “zero” the utility IMU. The registration tool(with the IMU 1002 locked in the holster) is removed from the anatomyand thereafter the utility IMU 1002 is removed from the registrationholster in preparation for mounting the IMU 1002 to other surgicaltools.

By way of example, the IMU 1002 previously mounted to the humeralregistration tool is removed from the tool and mounted to anothersurgical tool in a known location. In exemplary form, the IMU 1002(previously mounted to the humeral registration tool) is fixed rigidlyto a humeral resection block in a known location so that movement of theIMU 1002 correspondingly translates into known movement of the resectionblock. Given the reference IMU 1002 being fixedly mounted to the humerusin a known location, the IMUs work together to provide dynamicallyupdated information to the software system about changes in the position(via acceleration data) of both the humerus and resection block.

The software program provides a graphical user interface for a surgeonthat displays virtual models of the patient's humerus and a virtualmodel of the surgical tool in question, in this case a humeral resectionblock (the virtual model of the patient's humerus having already beencompleted pursuant to the virtual templating step, and the virtual modelof the resection block or other surgical tool having been previouslyloaded into the system for the particular resection block and othersurgical tools that may be utilized), and updates the orientation of thehumerus and surgical tool in real time via the graphical user interfaceproviding position and orientation information to the surgeon. Ratherthan using a graphical user interface, the instant system may includesurgical devices having indicator lights indicating to the surgeonwhether the resection block is correctly oriented and, if not, whatdirection(s) the resection block needs to be repositioned to correctlyorient the resection block to make the correct bone cuts consistent withthe pre-operative planning. In addition or alternatively, the utilityIMU 1002 may be mounted to a drill plate used to drill one or more holesinto each of which a reference pin is inserted. In such an instance, theresection block may not necessarily be accompanied by an IMU if thesurgical block is located and oriented properly using one or morereference pins. In any event, after making the requisite bone cuts, theutility IMU 1002 is removed from the surgical tool and fixed rigidly toa reamer (to correctly ream the humeral canal) and thereafter mounted toa humeral stem inserter with a known orientation relative to theinserter. The stem inserter is then utilized to place the humeral stemimplant within the reamed canal, with the IMUs continuing to provideacceleration feedback that the software utilizes to calculate positionof the humerus and stem inserter in real time and display this positiondata to the surgeon via the graphical user interface.

In addition to component placement, potential impingement of thecomponents can be tested using the IMUs mounted to the pelvis and femurto track component rotation to prevent post-operative complications andimprove overall patient satisfaction.

Pursuant to the foregoing disclosure of using IMUs 1002, the followingis an exemplary discussion of the mathematical model and algorithmsutilize to generate three dimensional position data from the gyroscopes,accelerometers, and magnetometers of each IMU. In exemplary form, eachIMU processor is programmed to utilize a sequential Monte Carlo method(SMC) with von Mises-Fisher density algorithm to calculate changes inposition of the IMU 1002 based upon inputs from the IMU's gyroscopes,accelerometers, and magnetometers. The IMU data stream consists of 1 setof gyroscopic data on three X, Y, Z axes (G1), 3 sets of accelerometersdata on X, Y, Z axes (A1-A3), and 3 sets of magnetometers data on threeX, Y, Z axes (M1-M3). Orientation tracking of the IMU 1002 may beaccomplished with one set of data from each sensors (i.e., G1, A1, M1).

Using G1, A1, and M1 as an example, and assuming all of the sensor rawdata has been converted and processed:

At time and state=1:

-   1) The algorithm first generates a set of N particles around the    neutral position with a pre-determined dispersion factor of the von    Mises-Fisher density, as represented by Algorithm 1 identified    below. Each particles represents the orientations around X, Y, Z    axis in quaternion form. In other words, the particles comprise a    set of independent and identically distributed random variables    drawn from the same probability density space. In orientation    tracking applications, the particles are the statistically    constrained variations of the observed orientations. But it should    be noted that the exact statistic (dispersion factor) does not need    to be ‘known’ as the algorithm optimizes its properties as it    gathers more samples. It is preferred to use a higher variability as    the initial guess and allow the algorithm to refine it.

Algorithm 1-Pseudo code to generate samplkes from von Mises- Fisherdensity Input: μ (Mean vector), κ (Dispersion factor), N (Number ofsamples/particles) 1. b = −κ + {square root over (κ² + 1)} 2.$x_{0} = \frac{1 - b}{1 + b}$ 3. c = κ(x₀) + 2log(1 − x₀x₀) 4. for n = 1→ N 5.  while t ≦ u 6.   while s ≦ 1 7.    uu~Π(−1, 1), vv~Π(0, 1) 8.   s = uu + vv   end 9.   $z = {\frac{1}{2} + {{uu}*{vv}*\frac{\sqrt{1 - s}}{s}}}$ 10.   u~Π(0, 1) 11.    $w = \frac{1 - {z\left( {1 + b} \right)}}{1 - {z\left( {1 - b} \right)}}$12.    t = κ(w) + 2 log(1 − x₀w) − c  end 13.  θ~Π(0, 2π), u~Π(−1, 1)14.  v = {square root over (1 − uu)} 15.  rand3DVec = v * cos(θ) v *sin(θ) u] 16.  q_(r) = w 17.  q_(x,y,z) = {square root over (1 − w²)} *rand3DVec 18.  q = [q_(r) q_(x) q_(y) q_(z)] 19.  q_(vMF)(n) = q

 μ End Return q_(vMF)

After the first data set are received from G1, A1, and M1, an estimateof the current orientation of the IMU is calculated. This isaccomplished by first knowing the tilt, which is measured from A1. Thetilt information is needed to mathematically correct (de-rotate) themagnetometers readings, as depicted as steps 2 and 3 in Algorithm 2identified below. Thereafter, the A1 and M1 data is used to estimate theinitial orientation of the IMU via Algorithm 2, which is based on aGauss Newton optimization method. The goal of Algorithm 2 is toiteratively determine the orientations (q_(obv)) so that the tilt andheading components of the estimation are the same as the reading from A1and M1 with acceptable margins of error. It should be noted that whileAlgorithm 2 requires an input from a previous state, but since there isno previous state at time=1, any input will suffice. The reason thataccelerometers and magnetometers cannot be used solely for trackingorientations is the limitation of how accelerometers measures tilts. Byway of example, it is possible that in several specific orientation,because of the nature of trigonometry quadrants, the outputs of tilt maybe the same despite the IMU being in different orientations. Thus, thegyroscopes are necessary to keep track of which quadrants the IMU is in.

Algorithm 2-Pseudo code calculate observation quaternation based onGauss Newton method Input: A_(i), i = x, y, z (Accelerometers data),M_(i), i = x, y, z (Magnetometers data), N (Number of steps), q_(obv)(Observation quaternation from previous state) 1. for n = 1 → N 2. h_(1,2,3,4) = q_(obv)

 {[0 M_(x) M_(y) M_(z)]

 conj(q_(obv))} 3.  $b_{r,x,y,z} = \left\lbrack {0\mspace{14mu} \sqrt{h_{2}^{2} + h_{3}^{2}}\mspace{14mu} 0\mspace{14mu} h_{4}} \right\rbrack$4.  Compute Jacobian matrix 5.  Compute rotational matrix R of q_(obv)  Perform Gauss Newton step 6.    ${qn}_{x,y,z,r} = {{q_{{obv}_{x,y,z,r}}^{T} - {\left( \frac{1}{J^{T}J} \right){J^{T}\left( {y_{e} - {M\left( y_{b} \right)}} \right)}\mspace{14mu} {where}\mspace{14mu} y_{e}}} =}$$\begin{bmatrix}0 & 0 & 1 \\b_{x} & b_{y} & b_{z}\end{bmatrix},{y_{b} = \begin{bmatrix}A_{x} & A_{y} & A_{z} \\M_{x} & M_{y} & M_{z}\end{bmatrix}},{M = \begin{bmatrix}R & \; \\\; & R\end{bmatrix}}$ 7.    Normalize qn_(x,y,z,r) 8.    q_(obv) =qn_(r,x,y,z) end Return q_(obv)

Next, the set of N particles in neutral position (q_(vMF)) are ‘rotated’so that their mean is centered on the orientation estimation from A1 andM1, pursuant to the following equation:

q _(est,i)(t)=q _(vMF)

q _(obs)(t), i=1 . . . N

Thereafter, all the particles are estimated forward in time based on G1,using the following equation:

q _(est,i)(t+1)=q _(est,i) ^(K)(t)+0.5(q _(est,i) ^(K)(t)

[0ω_(x)ω_(y)ω_(z)])Δt, i=1 . . . N

where ω are the angular rate measured at time t, and Δt is the samplingperiod.

In other words, if G1 is indicating an angular velocity around X axis;all the particles will be rotated around X axis based on the Newton'sequations of motion.

The orientations expectation in the current state is achieved byaveraging the particles estimate (q_(est,i)(t+1)) with Algorithm 3,identified below. Because a quaternion is a four dimensional vector, theaveraging is done in a different manner. Algorithm 3 iterativelyinterpolates two quaternions from the particle sets until only oneremains.

Algorithm 3-Pseudo code for calculating the expectation from a set ofquaternions Input: q_(est,i) = 1 → N (Estimation data). w_(i), i = x, y,z (Data weights), N (Number of particles) 1. for x = 1 → log(N)/log(2)2.  for k = 1 → (size(q_(est,i)))/2 3.   w_(n) = w_(2k−1)/(w_(2k−1) +w_(2k)) 4.   θ = acos(q_(est,2k−1) · q_(est,2k)) 5.   $q_{V,k} = {{q_{{est},{{2k} - 1}}\left( \frac{\sin \left( {\left( {1 - w_{n}} \right)\theta} \right)}{\sin \; \theta} \right)} + {q_{{est},{2k}}\left( \frac{\sin \left( {\left( w_{n} \right)\theta} \right)}{\sin \; \theta} \right)}}$6.   w_(V,k) = w_(2k−1)(w_(n)) + w_(2k)(1 − w_(n))  end 7.  q_(V,k) →q_(est,i), i = 1 → k 8.  w_(V,k) → w_(i), i = 1 → k end Return q_(V)

At time and state=2, the second data set is received. Using the samemethod (Algorithm 2) as described in paragraph [0380], the latestorientation estimation is calculated, which is then compared to all theparticles estimates from previous state (q_(est,i)(t−1)). Theerrors/residuals between each particles and the current orientationestimate are used to weight the accuracy of the particles (i.e., theparticles closer to the estimation will receive higher weight thanparticles further away.) using the following equations:

q_(res, i)(t) = q_(est, i)(t) ⊗ conj(q_(obs)(t)), i = 1  …  Nδ_(res, i) = 2cos (q_(res, i)(t) ⋅ q₀)$w_{i} = \frac{1/\delta_{{res},i}}{\sum\limits_{i}^{N}\left( {1/\delta_{{res},i}} \right)}$δ_(res, i)  is  the  residual  3D  angle  difference  between  the  particle  and  current  observation.w_(i)  is  the  weight  of  the  particle.

Next, the quality of the particles is evaluated to eliminate andresample particles having very low weight. This can be done by using adeterministic, a residual or an auxiliary resampling scheme. As thealgorithm favors particles closer to the observation, the particle setwill begin to lose diversity over time. The particles will become highlyconcentrated and no longer carry any statistical meaning. At that time,a small portion of the particles will be replaced to increase diversity.This is done by first evaluating the current dispersion factor of theparticles. If the dispersion factor indicates a high concentration, aset of new particles are generated in neutral position based on apredetermined dispersion factor to replace a portion of the currentparticles. The new particles are rotated from the neutral position tothe current orientation expectation. This is summarized in the followingequation:

${q_{{rs},i}(t)}\text{∼}{{{vMF}\left( {f\left( \sqrt{\sum\limits_{i}^{N}\delta_{{res},i}^{2}} \right)} \right)} \otimes \left( q_{\exp {({t + 1})}} \right)}$${{{where}\mspace{14mu} {f\left( \sqrt{\sum\limits_{i}^{N}\vartheta_{{res},i}^{2}} \right)}} = {{ae}^{- {b{(\sqrt{\sum\limits_{i}^{N}\vartheta_{{res},i}^{2}})}}} + {ce}^{- {d{(\sqrt{\sum\limits_{i}^{N}\vartheta_{{res},i}^{2}})}}}}},{i = {1\mspace{14mu} \ldots \mspace{14mu} N}}$

In addition, because this SMC method algorithm is temporal dependent, adelay in the received signal or temporarily losing connection to the IMUdata stream can produce adverse effects on the estimation. If connectionto the IMU data stream is not closely monitored, the particle set candiverge and destabilize the filter. This SMC method algorithm tracks theproperties of the particle sets after each iteration to prevent excessdivergence.

Finally, the particles are estimated forward in time based on new datafrom G1 and the current orientation state is calculated again. Theforegoing process and algorithms are reused each time new data from G1,A1, and M1 are received.

Creation of Trauma Plates

Referring to FIGS. 112-125, an exemplary process and system aredescribed for creating bone plates (i.e., trauma plates) across apredetermined population. Those skilled in the art are aware that boneis able to undergo regeneration to repair itself subsequent to afracture. Depending on the severity and location of the fracture, priorart trauma plates were utilized that often required bending or othermodifications in the operating room to conform to an irregular boneshape and achieve maximum contact between the bone fragments. However,excessive bending decreases the service life of the trauma plate, whichmay lead to bone plate failure and/or trauma plate-screw fixationloosening. The instant process and system provides a more accuratetrauma plate shape to reduce or eliminate having to contour the plateinteroperatively, thereby increasing plate service life and increasingthe time until any bone plate-screw fixation loosening occurs.

The foregoing exemplary explanation for creating trauma plates isapplicable to any and all bones for which trauma plates may be applied.For purposes of brevity, the exemplary explanation describes the systemand process for creation of a trauma plate for use with the humerusbone. But it should be understood that the process and system is equallyapplicable to other bones of the body and fabrication of correspondingtrauma plates and is in no way restricted to humerus trauma plates.

As part of the exemplary process and system for creating trauma plates,a statistical bone atlas is created and/or utilized for the bone(s) inquestion. By way of explanation, the bone in question comprises ahumerus. Those skilled in the art are familiar with statistical atlasesand how to construct a statistical atlas in the context of one or morebones. Consequently, a detailed discussion of constructing thestatistical bone atlas has been omitted in furtherance of brevity.Nevertheless, what may be unique as to the statistical bone atlas of theexemplary system and process is categorizing humeri within thestatistical bone atlas based upon gender, age, ethnicity, deformation,and/or partial construction. In this fashion, one or more trauma platesmay be mass customized to one or more of the foregoing categories, wherethe one or more categories establish a particular bone population.

In exemplary form, the statistical bone atlas includes anatomical datathat may be in various forms. By way of example, the statistical boneatlas may include two dimensional or three dimensional images, as wellas information as to bone parameters from which measurements may betaken. Exemplary atlas input data may be in the form of X-ray images, CTscan images, Mill images, laser scanned images, ultrasound images,segmented bones, physical measurement data, and any other informationfrom which bone models may be created. This input data is utilized bysoftware accessing the statistical atlas data to construct threedimensional bone models (or access three dimensional bone models havingalready been created and saved as part of the statistical atlas), fromwhich the software is operative to create a mean bone model or templatebone model in three dimensions.

Using the template bone model, the software can automatically designateor allows manual designation of points upon the exterior surface of thetemplate bone model. By way of explanation, in the context of the meanhumerus model, a user of the software establishes a general boundaryshape for the eventual trauma plate by generally outlining the shape ofthe trauma plate on the exterior surface of the humerus model. Thegeneral boundary shape of the trauma plate can also be accomplished bythe user designating a series of points on the exterior surface of thehumerus model that correspond to an outer boundary. Once the outerboundary or boundary points are established, the software mayautomatically designate or allows manual designation of points on theexterior surface of the humerus model within the established boundary.By way of example, the software provides a percent fill operation uponwhich the user can designate that percentage within the boundary of thetrauma plate to be designated by a series of points, each correspondingto a distinct location on the exterior of the humerus model. Inaddition, the software provides a manual point designation feature uponwhich the user may designate one or more points upon the exteriorsurface of the humerus model within the boundary. It should be notedthat in cases where manual point designation is utilized, the user neednot establish a boundary as a prefatory matter to designating pointsupon the exterior of the humerus model. Rather, when the manualdesignation of points is completed, the boundary is established by theoutermost points designated.

After the designation of points on the exterior surface of the templatebone model, the localized points are propagated throughout the bonepopulation in question. In particular, the localized points areautomatically applied to each three dimensional bone model within thegiven population by the software via point correspondence of thestatistical atlas. By way of example, the given bone population may begender and ethnic specific to comprise humeri from Caucasian women.Using the propagated points for each bone model of the population, thesoftware fills in the voids between points within the boundary using athree dimensional filling process to create a three dimensionalrendering of the trauma plate for each bone. Thereafter, the softwarecalculates the longitudinal midline of the three dimensional renderingof each trauma plate via a thinning process.

The midline of each three dimensional trauma plate rendering comprises athree dimensional midline having various curvatures along the lengththereof. The software extracts the three dimensional midline and, usinga least square fitting, determines the preferred number of radii ofcurvature that cooperatively best approximate the predominant curvatureof the three dimensional midline. In the context of humeri, it has beendetermined that three radii of curvature accurately approximate themidline curvature. But this number may vary depending upon the bonepopulation and the boundary of the trauma plate. Additional features canbe included here as well, such as cross-sectional curvature at one ormore locations along the length of the plate, location of muscles,nerves and other soft tissues to avoid, or any other feature relevant todefining plate size or shape. By way of example, the three radii ofcurvature for the midline represent the bend in the trauma plate in theproximal humerus, the transition between the humeral shaft and thehumeral head, and the curvature of the humeral shaft. Each radii ofcurvature is recorded and a four dimensional feature vector was appliedto the radii of curvature data to cluster the radii into groups thatbest fit the population. In exemplary form, the cluster data mayindicate that multiple trauma plates are necessary to properly fit thepopulation. Once the radii of curvature data is clustered, the traumaplate dimensions may be finalized.

Upon feature extraction related to the plate design, the softwaredetermines the best number of clusters that fits the population. It mustbe noted that there are some instances where there are two or moreclusters that provide local minima as outlined in FIG. 120. In order todetermine the optimum choice that provides acceptable error tolerance aswell as reasonable number of plates in each family, the softwaregenerates three dimensional surface model for the plates in eachclusters. Automatic evaluation is then performed by placing those plateson the population and computing the mismatch between the plate and thebone surface. Results of this analysis allow the software to pick theoptimal number of plates to be used for this specific population. Thefinal plate models are then parameterized and screw locations are placedon each plate in such a fashion as to avoid muscle and soft tissuelocations as well as maximize fixation. The width of the screws aredetermined by the cross sectional analysis of the bone at each screwlevel across the population.

The instant process and method was validated for the humerus using acadaver study. In particular, CT scans were taken of cadaver humerusbones from Caucasian white females. These CT scans were utilized by thesoftware to create separate three dimensional models for each humeri. Itshould be noted that neither the CT scans nor the three dimensionalmodels utilized during this validation study were part of thestatistical atlas and relevant population utilized to create the humeraltrauma plates. Consequently, the CT scans nor the three dimensionalmodels comprised new data and models used to validate the humeral traumaplates designed. After the three dimensional validation models had beengenerated, each of the models was categorized to a particular cluster(the clusters resulting from designing the humeral trauma plate from thedesign population). Based upon which cluster the validation model wascategorized to, the designed humeral trauma plate for that cluster wasfitted to the appropriate validation three dimensional humeral bonemodel and measurements were calculated showing any spacing between theexterior surface of the validation three dimensional humeral bone modeland the underside surface of the humeral trauma plate. FIG. 124 depictsa distance map of the trauma plate fitted upon to the validation threedimensional humeral bone model to show areas of maximum distance betweenthe bone and trauma plate. It can be seen that a majority of the traumaplate is minimally spaced from the bone, while areas of less conformityonly show spacing that ranges between 0.06 and 0.09 centimeters.Consequently, it was determined at the conclusion of this cadaver studythat the trauma plates designed pursuant to the foregoing exemplaryprocess using the foregoing system had extraordinary contour matchingthat, when applied intraoperatively, obviated the practice of surgeonshaving to bend or manually reshape bone plates.

Referencing FIGS. 131-138, in another exemplary instance of thisprocess, trauma plates were created for the clavicle. Here, astatistical atlas was created from numerous clavicle bones, whichsufficiently captured the variation within Caucasian population, forexample. It should be noted that the statistical atlas may includeclavicle bones from numerous ethnicities, from numerous ages ofpatients, and from various geographical regions. The exemplarydisclosure happens to be in the context of a Caucasian population dataset, though those skilled in the art will understand that the system andmethods described are not limited to only a Caucasian populationstatistical atlas. FIG. 132 depicts a generic clavicle anatomy.

In exemplary form, the statistical atlas of clavicle bones also defineslocations relating to muscle attachment sites for each clavicle, asdepicted in FIG. 134. In addition, cross-sectional contours wereextracted at 10% increments along the entire bone (see FIG. 138), aswell as at muscle attachment sites and at the clavicle waist (see FIG.137). Maximum and minimum dimensions of each cross-sectional contourwere calculated. In addition, the entire three-dimensional surface wasexamined for asymmetry by analyzing the magnitude and directionaldifferences between homologous points across all clavicle surfaces inthe dataset. The results confirm the existing studies on clavicleasymmetry, namely that the left clavicle is longer than the right, butthe right is thicker than the left. However, the patterns of asymmetrydiffer between males and females, as depicted in FIG. 139.

Additionally, as shown in FIG. 133, the clavicle midline does not followa symmetrical “S” shape, as is the case for existing clavicle traumaplate designs. Thus, the present disclosure confirms that present dayclavicle trauma plates fail to mimic the anatomical curvature of theclavicle. With respect to FIGS. 135 and 136, male clavicles aresignificantly asymmetric in all dimensions and at muscle and ligamentattachment site contours (p>0.05), whereas female asymmetry is morevariable. However, an area with no muscle attachments on the posteriormidshaft was significantly asymmetric in both sexes.

From the extracted clavicle features across the statistical atlas,clustering (in accordance with previously described methods ofclustering in the instant application, which are incorporated herein byreference) was performed to determine distinct groupings of similarities(i.e., a population) from which each distinct group was associated witha particular clavicle trauma plate to optimally fit the population.Additionally, screw fixation locations and length were determined foreach trauma plate population to optimally avoid soft tissues (muscleattachments) and prevent additional fractures or plate loosening as aresult of screws that are too long or too short. Using the process,several clavicle trauma plate families were designed corresponding tomass-customized clavicle trauma plates, as depicted in FIGS. 140-149.

Creation of Patient-Specific Trauma Plates

Referencing FIG. 126, a patient-specific trauma process is graphicallydepicted to include various component parts. Among these component partsare pre-operative surgical planning, generation of pre-contouredpatient-specific trauma plate(s), intra-operative guidance to positionand secure the patient-specific trauma plate(s), and optionalpost-operative evaluation of the patient-specific trauma plate(s). Amore detailed discussion of these component parts and the exemplaryprocess and structures involved for each component part is discussed inturn.

Referring to FIG. 126-130, an exemplary process flow is depicted for thepre-operative surgical planning component part. An initial input ofanatomical data is obtained for the anatomy in question. For purposes ofexemplary illustration only, a clavicle will be described as thefractured or deformed anatomy and a clavicle trauma plate will bedescribed as the patient-specific trauma plate. Anatomical data is inputto a software package configured to select or create patient-specificclavicle trauma plate, where the anatomical data comprises twodimensional (2D) images or three dimensional (3D) surfacerepresentations of the clavicle that may, for example, be in the form ofa surface model or point cloud. In circumstances where 2D images areutilized, these 2D images are utilized to construct a 3D virtual surfacerepresentation of the fractured clavicle. Those skilled in the art arefamiliar with utilizing 2D images of anatomy to construct a 3D surfacerepresentation. Accordingly, a detailed explanation of this process hasbeen omitted in furtherance of brevity. By way of example, inputanatomical data may comprise one or more of X-rays, computed tomography(CT) scans, magnetic resonance images (Mills), or any other imaging datafrom which a 3D surface representation of the tissue in question may begenerated. The output from this anatomical data input is a 3D virtualsurface representation of the fractured clavicle component parts.

The 3D virtual surface representation of the fractured claviclecomponent parts is then evaluated to identify the location and shape ofthe fracture or, in the case of a complete fracture and separation ofbone component parts, the location and shape of the bone components withrespect to one another.

In the circumstance of a complete fracture and separation of bonecomponent parts, the process and associated software carries out afracture reduction process that may allow for manual repositioning ofthe 3D virtual surface representation of the fractured clavicle toconstruct a patchwork clavicle. In such a circumstance, a userrepositions and reorients the 3D virtual surface representations of thefractured clavicle to create a 3D patchwork clavicle model resembling aclavicle assembled from component parts comprising the 3D virtualsurface representations. Alternatively, the process and associatedsoftware may provide for automatic repositioning and reconstruction ofthe 3D virtual surface representations of the fractured clavicle toconstruct a patchwork clavicle model, optionally using a 3D templatemodel of a clavicle. More specifically, the software initially detectsone or more fracture sites from the 3D virtual surface representationfor each fractured bone component (i.e., the edge(s) of the bonefracture) comprising the 3D virtual surface representation and extractsthe contours from each fracture site. The software then compares theextracted contours with the contours of a 3D template clavicle model inorder to match, in a pair wise manner, these contours and locatematching bone components/pieces for each fracture site. Those matchedcomponents/pieces are then grouped together. Following grouping of thematched components/pieces, the software matches the grouped pieces tothe 3D template clavicle model to identify the correct location of allthe bone components/pieces in relation to the 3D template claviclemodel. The matched components/pieces are thereafter reduced into a 3Dpatchwork clavicle model resembling the 3D template clavicle model,which as discussed hereafter is utilized by the software to construct a3D reconstructed clavicle model.

After reduction, referring back to FIGS. 7 and 127, the 3D patchworkclavicle is used to identify the anatomical model (e.g., complete bonemodel) in the statistical atlas that most closely resembles the 3Dpatchwork clavicle model of the patient in question. This step isdepicted in FIG. 3 as finding the closest bone in the atlas. In order toinitially identify a bone model in the statistical atlas that mostclosely resembles the 3D patchwork clavicle model, the 3D patchworkclavicle model is compared to the bone models in the statistical atlasusing one or more similarity metrics. The result of the initialsimilarity metric(s) is the selection of a bone model from thestatistical atlas that is used as an “initial guess” for a subsequentregistration step. The registration step registers the 3D patchworkclavicle model with the selected atlas bone model (i.e., the initialguess bone model) so that the output is a patient-specific reconstructedbone model that is aligned with the atlas bone model. Subsequent to theregistration step, the shape parameters for aligned “initial guess” areoptimized so that the shape matches the 3D patchwork clavicle model.

Shape parameters, in this case from the statistical atlas, are optimizedso that regions of non-fractured bone are used to minimize the errorbetween the reconstructed patient-specific bone model and 3D patchworkclavicle model. Changing shape parameter values allows forrepresentation of different anatomical shapes. This process is repeateduntil convergence of the reconstructed shape is achieved (possiblymeasured as relative surface change between iterations or as a maximumnumber of allowed iterations).

A relaxation step is performed to morph the optimized bone to best matchthe 3D patchwork clavicle model. Consistent with the exemplary case, themissing anatomy from the 3D patchwork clavicle model that is output fromthe convergence step is applied to the morphed 3D clavicle model,thereby creating a patient-specific 3D model of the patient'sreconstructed clavicle. More specifically, surface points on the 3Dpatchwork clavicle model are relaxed (i.e., morphed) directly onto thepatient-specific 3D clavicle model to best match the reconstructed shapeto the patient-specific shape. The output of this step is a fullyreconstructed, patient-specific 3D clavicle model representing whatshould be the normal/complete anatomy of the patient's clavicle.

Following full anatomy reconstruction, the system software initiates aplan reduction order process. In this plan reduction order process, thesoftware allows for manual or automatic determination of which claviclebone component parts (i.e., fractured clavicle bone pieces) will bereassembled and mounted to one another, and in what order. In so doing,the software records in memory a 3D model of the progressive assembly ofthe clavicle from the bone component parts. Thus, presuming the clavicleis fractured into six component parts, the software would record a first3D model showing assembly of the first and second bone fracturedcomponent parts being assembled, followed by a second 3D model showingassembly of the first, second, and third bone fractured component partsbeing assembled, and so on until arriving at a final 3D model reflectingthe assembled position and orientation of all six fractured bonecomponent parts, thereby resembling the 3D patchwork clavicle model.

Using the reduction order determination, the software allows manual orautomatic selection from one of a plurality of clavicle trauma platetemplates using the 3D patchwork clavicle. More specifically, theclavicle trauma plate templates comprise a series of 3D virtual surfacerepresentations of clavicle trauma plates having been generically shapedto match the size and shape parameters associated with a givenpopulation taken from a statistical bone atlas. In other words, thestatistical bone atlas includes surface models of a plurality of normal,full anatomy clavicles having been categorized based upon one or more ofsize, ethnicity, age, sex, and any other marker indicative of boneshape. An exemplary discussion of the procedure to arrive at thetemplate bone plates has been previously described with respect to FIGS.112-125 and is incorporated herein by reference. In the automaticselection mode, the software compares the dimensions and contours of theplurality of clavicle trauma plate templates to the 3D patchworkclavicle to discern which of the templates most closely conforms to the3D patchwork clavicle (i.e., contour and shape similarity with respectto the bony anatomy).

Using the clavicle trauma plate template that most closely conforms tothe 3D patchwork clavicle, the software allows for manual or automaticidentification of fixation site locations through the trauma plate aswell as determining direction and length of fixation devices to beutilized (e.g. surgical screws). In automatic fixation siteidentification mode, the software accounts for muscle and attachmentlocations, as well as nerve locations, to avoid placing any fixationhole in the path of a nerve or muscle attachment site. In addition, thesoftware allows for manual or automatic selection of fixation fastenersto be used with the trauma plate. In this manner, the software mayautomatically select fasteners taking into account the size and shape ofthe clavicle bone fracture components, the location and orientation ofthe fastener holes extending through the trauma plate, and the geometryof the fasteners (e.g., screws) so as to increase fixation strength andattempting to avoid unnecessary compromises in clavicle bone integrity.

After selection of the clavicle trauma plate template, the fixation holelocation(s), and the fixation fasteners, the software carries out avirtual bone plate placement. This includes positioning the clavicletrauma plate template onto the 3D patchwork clavicle and manually orautomatically deforming the clavicle trauma plate template to match theexterior surface contours of the 3D patchwork clavicle, thereby creatinga virtual 3D patient-specific clavicle trauma plate with size, length,and contour dimensions. The software logs the patient-specific clavicletrauma plate dimensions and converts these virtual dimensions intomachine code that allows for generation of a tangible patient-specificclavicle trauma plate that can be rapid manufactured.

Using the patient-specific clavicle trauma plate dimensions, thesoftware also receives anatomical data as to the position and locationof the patient's soft tissue, vessels, and nerves within the area of thefractured clavicle to construct an incision plan. The incision plan ispre-operative and suggests a surgical approach to make one or moreincisions that increases access to the fractured clavicle bone componentparts, while at the same time decreases the invasiveness of the surgicalprocedure, thereby potentially decreasing recovery time and ancillarypost-operative trauma. FIG. 134 shows a 3D patchwork clavicle havingsurface coloring indicative of locations where muscle attaches to thepatient's clavicle. Consequently, the patterned circles extendinglongitudinally along the 3D patchwork clavicle correspond to thelocation of fixation fasteners, which are oriented to locationspredominantly free of muscle attachment.

After the incision plan is constructed, a surgeon reviews the incisionplan to make any modifications prior to approval of the plan. Postapproval of the incision plan, the plan may be exported to anintraoperative surgical guidance system. Likewise, the incision plan maybe utilized to construct a preoperative tangible clavicle model forestimating the shape of the reconstructed clavicle bone componentsmounted to one another to simulate the patient's normal clavicle. Thistangible clavicle model may then be used to test fit the clavicle traumaplate and make any contour modifications via bending that may be desiredby the surgeon preoperatively. Alternatively, tangible clavicle modelmay comprise the clavicle bone components in loose form so that mountingone or more of the trauma plates thereto is necessary to hold theclavicle bone components together, thereby allowing the surgeon to testfit the trauma plate(s) ex-vivo and also make any modifications to thetrauma plate(s) ex-vivo.

Referencing FIGS. 128 and 129, the exemplary patient-specific clavicletrauma plate(s) may be positioned intraoperatively using fluoroscopy.While the exemplary technique will be described with respect toattaching a patient-specific clavicle trauma plate to a patient'sclavicle or clavicle bone component parts, it should be understood thatthe exemplary process is equally applicable to attachingnon-patient-specific trauma plates to a clavicle and, more generally, toattaching any trauma plate to any bone or fractured bone component part.

FIG. 128 depicts a process flow depicts various steps involved as partof a trauma plate placement system for positioning a patient-specifictrauma plate intraoperatively using fluoroscopy, which includesutilizing pre-planning data along with placement of fudicial markers toestablish a patient location registration. More specifically, thepre-planning data is loaded into a software package of the trauma plateplacement system and may include patient geometries bone and tissuegeometries, location of each trauma plate, the type and location offixation devices utilized to secure the trauma plate to the bone or bonecomponent part in question, and any other relevant information bearingon operative location and techniques. Fudicial markers for use withfluoroscopy include, without limitation, optical, electromagnetic, IMUs(though optical markers are referenced in the process flow of FIG. 128,which are positioned at known locations relative to anatomical landmarkson the patient. Using the fudicial markers and known anatomicallocations and dimensions of the patient, the trauma plate placementsystem registers the patient with respect to a pre-operative coordinatesystem. Thereafter, the fudicial markers are tracked in space so thatfeedback from the trauma plate placement system is provided to thesurgeon consistent with the pre-operative plan indicating the locationof one or more incisions with respect to a fixed patient frame ofreference. Exemplary feedback systems that may be utilized as part ofthe trauma plate placement system include, without limitation, visualdisplays that are projected on to the surface of the patient outliningthe location and length of each incision.

In the context of a fractured clavicle, where the clavicle is comprisedof separate bone component parts, the trauma plate placement system isalso capable of visually displaying identification indicia on multipleclavicle bone components to indicate the order of assembly of the bonecomponents. In exemplary form, the visual display includes colorednumerals that are displayed on each bone component that is visible. Thecolored numerals change colors depending upon the orientation andlocation of the bone components with respect to one another. Inexemplary form, the first bone component is identified by a displayednumeral “1” that is projected onto the exterior surface. Depending uponthe orientation and position of the bone, the displayed numeral “1” maybe colored red, yellow, or green. A red numeral indicates theorientation and location are incorrect. Upon movement, the indiciachanges to yellow if the surgeon is moving the bone component in thecorrect direction to achieve placement consistent with the pre-operativeplan. Upon continued movement, the numeral turns green when the properlocation is achieved. This repositioning process is repeated for each ofthe clavicle bone components.

In order to provide this visual feedback to the surgeon regarding thelocation and orientation of the fractured bone components, the traumaplate placement system uses fluoroscopy to track the bone components in3D space to discern whether the bone location and orientation isconsistent with the pre-operative plan. Prior to bone componenttracking, the bone components are registered using pre-operative data inorder to provide real-time updated information to the surgeon, via theprojected display, as to the correct location and orientation of thebone components. As each bone fragment is tracked, and eventuallymounted to the clavicle trauma plate, the system confirms the progressof the trauma plate placement using fluoroscopic images to confirm theplate orientation and location as well as that of the fixation devices(e.g., screws) and bone components. Finally, when the bone componentsare coupled to one another via one or more clavicle trauma plates, thesystem displays a final indicia indicating to the surgeon that theprocedure has met the objectives of the pre-operative planning and canbe concluded.

FIG. 130 depicts a process flow diagram for various steps involved aspart of a trauma plate placement system for positioning apatient-specific trauma plate intraoperatively using ultrasound in lieuof fluoroscopy. The foregoing explanation with respect to FIG. 128parallels that of FIG. 130, which is incorporated herein by reference,with the exception of the system tracking bone components, traumaplate(s), and fixation devices using ultrasound in lieu of fluoroscopy.Consequently, a redundant explanation has been omitted in furtherance ofbrevity.

Creation of Trauma Plate Placement Guides

Referring to FIG. 150, an exemplary process and system are described forcreating trauma plate placement guides that are patient-specific. Thoseskilled in the art are aware that bone can fracture at one or morelocations resulting in bone fragments that are separated from oneanother. As part of reconstructive surgery to repair the bone, thesefragments are held in a fixed orientation using one or more traumaplates. Reconstructive surgeons attempted to piece the bone backtogether using innate knowledge rather than patient-specific anatomicalfact. Consequently, to the extent patient bone anatomy varied fromnormal, the bone fragments were grossly distorted, or the number of bonefragments was large, surgeons would resort to using prior art traumaplates and having the bone fragments match the shape of the plate ratherthan vice versa. The instant process and system improves upon prior arttrauma plate application by creation of trauma plate placement guidesand customized trauma plates that match the trauma plates to the bone toreplicate the original bone shape and orientation.

The exemplary system flow begins with receiving input datarepresentative of a fractured anatomy. For purposes of explanation only,the fractured anatomy comprises a human skull. It should be noted thatthe foregoing process and system is equally applicable to otheranatomies/bones including, without limitation, bones in the arms, legs,and torso. In exemplary form, anatomy data input may be in the form ofX-rays, CT scans, MRIs, or any other imaging data from which bone sizeand shape may be represented.

The input anatomy data is utilized to construct a three dimensionalvirtual model of the fractured anatomy. By way of example, the inputanatomy data comprises a computed tomography scan of a fractured skullthat is processed by software to segment this scan and generate a threedimensional model. Those skilled in the art are familiar with how toutilize computed tomography scans to construct three dimensional virtualmodels. Consequently, a detailed description of this aspect of theprocess has been omitted in furtherance of brevity.

Subsequent to generation of the three dimensional virtual model of thefractured skull, the software compares the three dimensional virtualmodel of the skull with data from a statistical atlas to determine areasin the three dimensional virtual model where the skull is fractured. Inparticular, the software utilizes features extracted from the surfacemodel of the input anatomy (ex: surface roughness, curvature, shapeindex, curvedness, neighbor connectivity) to extract areas of fracturesites. The outline contours of those fracture sites are then extractedand matched together to find the matching fracture sites. Fracturedfragments are also matched with the atlas to indicate the best locationto place the matched fracture sites in order to reconstruct the normalanatomy.

After the software generates a reconstructed three dimensional virtualmodel of the fractured skull, buttresses may be manually and/orautomatically positioned on the exterior of the reconstructed threedimensional virtual skull model. The automatic placement of thebuttresses is the result of programmed logic to maximize stability ofthe bone fragments while minimizing the number of buttresses. As usedherein, the term buttress and plurals thereof refer to any support usedto steady bone fragments with respect to one another. In certaininstances, practical experience by a surgeon or other learned user maysupplement or supplant to the logic when making use of the manualbuttress placement feature. In any event, a series of buttresses areprogrammed into the software that allows the software or a user of thesoftware to select differing buttresses for differing applications. Atthe same time, the length of the buttresses may be manually orautomatically manipulated based upon the dimensions of the fracture andbone fragments.

Subsequent to buttress assignment and placement on the reconstructedthree dimensional virtual skull model, the software dimensions andcontour of each buttress is recorded by the software. This recordationincludes information necessary for fabrication of each buttress or atthe very least information helpful to allow a surgeon or other learnedindividual to take existing buttresses and conform each to a placementguide. In the context of molding an existing buttress, the softwareextracts the contours of the reconstructed three dimensional virtualskull model to generate computer-aided design (CAD) instructions forcreation of one or more tangible models indicative of the reconstructedthree dimensional skull model. These CAD instructions are sent to arapid prototyping machine, which creates the one or more tangible modelsindicative of the reconstructed three dimensional skull model. Byrecreating the proper anatomical surface as a tangible model, eachbuttress may be applied to the tangible model at the target location andmanually conformed prior to implantation and fastening to the patient'sskull.

Based upon the location and length of any buttress, the software alsoextracts the contours of the reconstructed three dimensional virtualskull model to generate contour data for one or more patient-specificbuttress placement guides. In particular, a placement guide may begenerated for each buttress. In this manner, the placement guideincludes a surface contour that matches the contour of the patient'sskull in a single orientation. Given that the location of the buttressis known on the virtual model of the reconstructed skull, as is thecontour of the adjacent exterior skull surface, the software combinesthe two to create a virtual patient-specific placement guide. Thisvirtual guide is output in the form of CAD instructions to a rapidprototyping machine for fabrication.

In this exemplary embodiment, the fabricated patient-specific placementguide comprises an elongated handle configured to be gripped by asurgeon. Extending from the end of the elongated handle is a blockC-shaped contour plate. The underside of the contour plate is concave tomatch the convex topography of the skull at the location where thebuttress should be positioned. Though not required, the ends (or anotherportion) of the contour plate may be fastened to the buttress, or thecontour plate may simple provide a working window within which thebuttress is aligned and ultimately fastened to the skull. Postattachment of the buttress to the skull, the contour plate may beremoved.

Customized Cutting & Placement Guides, Plates

Referring to FIG. 151, reconstruction of a deformed, fractured, orpartial anatomy is one of the complex problems facing healthcareproviders. Abnormal anatomy may be the result of birth conditions,tumors, diseases, or personal injuries. As part of providing treatmentfor various ailments, healthcare providers may find it advantageous toreconstruct an anatomy or construct an anatomy to facilitate treatmentfor various conditions that may include, without limitation,broken/shattered bones, bone degeneration, orthopedic implant revision,orthopedic initial implantation, and disease.

The present disclosure provides a system and methods for bone and tissuereconstruction using bone grafts. In order to carry out thisreconstruction, the system and associated methods utilizes currentanatomy images of a patient to construct two virtual 3D models: (a) afirst 3D model representative of the current abnormal anatomy; and, (2)a second 3D model representative of the reconstructed anatomy of thepatient. Reference is had to the prior “Full Anatomy Reconstruction”section for a detailed explanation of using patient images (X-rays, CTscans, MRI images, etc.) to arrive at virtual models of the patient'sabnormal anatomy and reconstructed anatomy. The present system andmethods builds upon the system described in the “Full AnatomyReconstruction” section to utilize the two 3D virtual models incombination with constructing a 3D virtual model of one or more bonesfrom which a bone graft may be taken (i.e., a donor bone). As will bedescribed in more detail hereafter, the 3D virtual models of thepatient's reconstructed and abnormal anatomy are analyzed to generate a3D virtual model of the bone graft needed for reconstruction. This 3Dvirtual graft model is compared to the 3D virtual model of the donorbone to access one or more sites on the donor bone from which a bonegraft can be excised. After determining the excise location(s), cuttingguides and graft placement guides are designed and fabricated forgathering the grafted bone and mounting the grafted bone to the site ofreconstruction.

By way of exemplary explanation, the instant system and methods will bedescribed in the context of a facial reconstruction, where the donorbone comprises the fibula. Those skilled in the art should realize thatthe instant system and methods are applicable to any reconstructivesurgical procedure utilizing one or more bone grafts. Moreover, whilediscussing facial reconstruction and the fibula as the bone donor, thoseskilled in the art should understand that the exemplary system andmethods may be used with donor bones other than the fibula.

As a prefatory step to discussing the exemplary system and methods foruse with reconstructive surgical planning and surgical procedures usingbone grafts, it is presumed that the patient's abnormal anatomy has beenimaged and virtual 3D models of the patient's abnormal and reconstructedanatomy have been generated pursuant to those processes described in theprior “Full Anatomy Reconstruction” section. Consequently, a detaileddiscussion of utilizing patient images to generate both virtual 3Dmodels of the patient's abnormal and reconstructed anatomy has beenomitted in furtherance of brevity.

After virtual 3D models of the patient's abnormal and reconstructedanatomy have been created, the software compares the anatomies andhighlights areas of difference. In particular, the areas in commonbetween the virtual 3D models denotes bone that will be retained,whereas areas that differ is indicative of one or more sites forreconstruction. The software extracts from the virtual 3D model of thepatient's reconstructed anatomy those areas not in common and isolatesthese areas as separate 3D virtual models of the intended bone graft.The surgeon or other pre-operative planner may view the virtual 3D bonegraft models and use his judgment as to the bone or bones from which thebone grafts might be best excised.

Regardless as to the logic utilized to initially choose a possible boneas a graft candidate, the bone(s) in question is imaged usingconventional modalities (X-ray, CT, MRI, etc.). Using the processesdescribed in the prior “Full Anatomy Reconstruction” section, eachimaged bone is segmented and a virtual 3D model of the imaged bone iscreated. This 3D donor bone model is compared to the virtual 3D bonegraft model to isolate areas in common. In particular, the softwarecompares the surface contours of the 3D donor bone model with thesurface contours of the virtual 3D bone graft model to identify areas incommon or having similar curvature. Presuming no areas are in common orsimilar, the process can be restarted by analyzing another possibledonor bone. In contrast, if one or more areas in common or havingsimilar curvature exist in the donor bone, these areas are highlightedon the 3D donor bone model. In particular, the highlighted areas mimicthe shape of the virtual 3D bone graft model. If the area in common isjudged to be appropriate for excising the bone graft, the softwarevirtually excises the bone graft as a virtual 3D model and applies thebone graft (which has contours specific/unique as to the donor bone) tothe virtual 3D model of the patient's abnormal anatomy to verifypotential fit and any areas of the patient's abnormal anatomy that mayneed to be excised as part of the reconstruction. In circumstances whereapplication of the virtual 3D model of the excised bone to the virtual3D model of the patient's abnormal anatomy results less thansatisfactory reconstruction, the process may be restarted at the boneselection point or restarted to excise a different area of bone. Butpresuming application of the virtual 3D model of the excised bone to thevirtual 3D model of the patient's abnormal anatomy results in anappropriate fit, the system moves forward with designing jigs tofacilitate excising the bone graft and mounting the bone graft to thepatient's residual bone.

In this exemplary embodiment, the system generates and outputs machinecode necessary for a rapid prototyping machine, CNC machine, or similardevice to fabricate a bone graft cutting guide and a bone graftplacement guide. In order to generate the outputs necessary to fabricatethe bone graft cutting guide and a bone graft placement guide, thesystem utilizes the virtual 3D model of the excised bone to the virtual3D model of the patient's abnormal anatomy.

In particular, the virtual 3D model of the excised bone defines theboundary of a virtual 3D cutting guide. Moreover, in this exemplarycontext, a portion of the fibula is intended to be excised to providethe bone graft. In order to ensure the appropriate portion of the fibulais excised, the virtual 3D cutting guide includes a window within whicha cutting device (saw, cutting drill, etc.) traverses to create theappropriately outlined bone graft. Not only does the virtual 3D cuttingguide need to be shaped to create the appropriate bone graft outline,but it also needs to be shaped to ensure placement of the cutting guideon the patient's donor bone is particularized. More specifically, theplacement of the cutting guide on the donor bones needs to concurrentlyensure the excised bone includes the correct outline shape and alsoexhibits the correct contours. In this fashion, the underside of thevirtual 3D cutting guide is designed to be the “negative” of the surfaceof the donor bone where the cutting guide will be mounted. Exemplarymounting techniques for securing the cutting guide to the donor bone mayinclude, without limitation, screws, dowels, and pins. In order toaccommodate one or more of these mounting techniques or others, thevirtual 3D cutting guide is also designed to include one or more throughorifices besides the window within which the surgical cutter traverses.After the design of the virtual 3D cutting guide is completed, thesystem generates and outputs machine code necessary for a rapidprototyping machine, CNC machine, or similar device to fabricate thebone graft cutting guide, which is followed by fabrication of the actualcutting guide.

In addition to the cutting guide, the software also designs one or morebone graft placement guides. The bone graft placement guides arepatient-specific and conform to the anatomy of the patient (both donorbone and residual bone to which the donor bone is mounted) to ensurecorrect placement of the bone graft with respect to the residual bone.In exemplary form, the bone graft placement guide is configured for amandible bone reconstructive procedure. In order to design the bonegraft placement guides, the software utilizes the virtual 3D model ofthe excised bone applied to the virtual 3D model of the patient'sabnormal anatomy to construct a hybrid model. Using this hybrid model,joints are identified where the bone graft will interface with (andhopefully join via bone growth) the adjacent residual bone. At thesejoints, depending upon various factors, such as surgeon preference, thesystem identifies bone graft plate locations and, for each plate, one ormore guides to facilitate correct placement and securing of the platesto the bone graft and residual bone.

Customized Trauma Plate Templating and Placement Guides

Referring to FIG. 152, an exemplary system and method for trauma platetemplating are depicted graphically in the form of a flow diagram. Thissystem and method, which include a computer and associated software,makes calculations to determine the best fit among a group of templatetrauma plates in order to reduce future shape changes that may benecessary to fit the trauma plate to match a patient's bone geometry. Inexemplary form, the system includes constructing a 3D model of thepatient's fractured bone as a unified bone and then forming a templatetrauma plate to the 3D model to finalize the shape of the trauma plateprior to implantation. In this fashion, the final trauma plate shape ispatient-specific and allows for a closer fit to the patient's anatomy,eliminates ambiguity in placement location of the trauma plate, andshortens surgery time. The system can be easily deployed in everydayclinical environments or surgeon's offices.

Referring back to FIG. 152, the initial input to the system is anynumber of medical image depicting a fractured bone. By way of example,these medical images may be one or more of X-ray, ultrasound, CT, andMM. The images of the fractured bone are analyzed by human operator toselect which bone, among a plurality of possible programmed bones, isfractured. Using the bone selection, the software utilizes the medicalimage data to form 3D models of the fractured bone components (aspreviously described with respect to FIG. 127 and its associateddescription, which is incorporated herein by reference). These 3D bonemodels are then reduced (i.e., reassembled to form a patchwork boneorienting and locating the 3D bone models as if connected to one anotherwhen part of a unified, unfractured bone) to form a 3D patchwork bonemodel using bone data from a statistical atlas. Likewise, bone data fromthe statistical atlas is also used in combination the 3D patchwork bonemodel to morph the 3D patchwork bone model onto a complete, unfracturedbone model to generate a complete, 3D bone model (unfractured) of thepatient's bone in question, referred to as the reconstructed bone model.

This reconstructed bone model is analyzed by the software to extractlongitudinal curves (e.g., midline curves) along the dominant dimension,while the software also extracts cross-sectional curves takenperpendicular to the dominant dimension, in order to extract traumaplate design parameters. From these design parameters, the softwarecalculates which, among a plurality of template trauma plates, mostclosely resembles the design parameters. These design parameters mayinclude length of the trauma plate, longitudinal curvature of the traumaplate, lateral curvature perpendicular to the longitudinal curvature,lateral length, and fixation locations for bone fasteners that minimizeinterference with muscle attachment sites and nerve locations, while atthe same time ensuring proper mounting and retention of the trauma plateto the fractured bone.

The reconstructed bone model is also utilized to generate a tangible, 3Dbone model. In exemplary form, the software is programmed to output thevirtual reconstructed bone model as machine code, thereby allowing rapidprototyping of the 3D bone model, either in an additive or subtractiveprocess. For purposes of the instant disclosure, an additive processincludes 3D printing where the model is created from a starting blankcanvas by the addition of material to form discrete layers or slices ofthe bone model that, once stacked upon one another by printingsuccessive layers, form the final bone model. In contrast, a subtractiveprocess includes starting with a solid block of material and, usingmachine code (e.g., CNC code) to machine away material to arrive at asolid bone model. Those skilled in the art will understand that anynumber of processes may be utilized to fabricate a tangible bone model.Depending upon the process chosen, the software is programmed to convertthe 3D virtual model into machine code to facilitate rapid prototypingand construction of the 3D bone model.

Post 3D bone model construction, the template trauma plate may beconstructed, machined, or selected based upon the selection of thesoftware as to the trauma plate most closely shaped to conform to thepatient's fractured bone. Once at hand, the template trauma plate isfitted to the 3D bone model and further refined by manual bending toconform the trauma plate to the 3D bone model. After sufficientconformity between the trauma plate and bone model, the trauma plate maybe considered patient-specific and, post sterilization, is ready forimplantation into the patient.

Patient-Specific Hip Cage Templating and Placement Guides

Referring to FIG. 153, an exemplary system and method for hip cagetemplating and placement guides are depicted graphically in the form ofa flow diagram. This system and method, which include a computer andassociated software, makes calculations to determine the best fit amonga group of template hip cages in order to reduce future shape changesthat may be necessary to fit the hip cage to match a patient's bonegeometry. In exemplary form, the system includes constructing a 3D modelof the patient's hip (as a unified bone if fractured or degenerated) andthen forming a template hip cage to the 3D model to finalize the shapeof the hip cage prior to implantation. In this fashion, the final hipcage shape and attachment sites are patient-specific and allows for acloser fit to the patient's anatomy, eliminates ambiguity in placementlocation of the hip cage, and shortens surgery time. The system can beeasily deployed in everyday clinical environments or surgeon's offices.

Referring back to FIG. 153, the initial input to the system is anynumber of medical images depicting the patient's hip (total or partialpelvis). By way of example, these medical images may be one or more ofX-ray, ultrasound, CT, and MM. The images of the hip bone are utilizedby the software to construct a 3D virtual bone model of the patient'ship (as previously described with respect to FIGS. 1 and 7 and itsassociated description, which is incorporated herein by reference). This3D bone model is then automatically landmarked by the software.

The software utilizes inputs from the statistical atlas (e.g., regionslikely to contain a specific landmark) and local geometrical analyses tocalculate anatomical landmarks for 3D bone model in comparison to thosehip bone models within the statistical atlas. This calculation isspecific to each landmark. The approximate shape of the region is known,for example, and the location of the landmark being searched for isknown relative to the local shape characteristics. For example, locatingthe superior margin of the anterior labral sulcus point of theacetabulum is accomplished by refining the search based on theapproximate location of superior margin of the anterior labral sulcuspoints within the statistical atlas. This process is repeated for eachlandmark in question.

After the anatomical landmarks are automatically calculated for the 3Dbone model, the bone model is analyzed by the software to calculatewhich, among a plurality of template hip cages, most closely fits theanatomical landmarks. In addition to calculating which, among aplurality of hip cages, most closely fits the anatomical landmarks ofthe patient's hip, the software also calculates the location where thecage will be mounted to the patient's anatomy. Referring back to FIGS.20 and 21, the associated discussion of which is incorporated herein byreference, the software is operative to determine the location where thecage will be mounted to the patient's anatomy, as well as generatevirtual 3D guides that may be utilized to output machine code sufficientto construct a tangible 3D placement guide for the revision cage.

The bone model of the patient's hip is also utilized to generate atangible, 3D bone model. In exemplary form, the software is programmedto output the virtual 3D bone model as machine code, thereby allowingrapid prototyping of the tangible 3D bone model, either in an additiveor subtractive process. For purposes of the instant disclosure, anadditive process includes 3D printing where the model is created from astarting blank canvas by the addition of material to form discretelayers or slices of the bone model that, once stacked upon one anotherby printing successive layers, form the final bone model. In contrast, asubtractive process includes starting with a solid block of materialand, using machine code (e.g., CNC code) to machine away material toarrive at a solid bone model. Those skilled in the art will understandthat any number of processes may be utilized to fabricate a tangiblebone model. Depending upon the process chosen, the software isprogrammed to convert the 3D virtual model into machine code tofacilitate rapid prototyping and construction of the 3D bone model.

Post 3D bone model construction, a template cage may be constructed,machined, or selected based upon the selection of the software as to thecage most closely shaped to conform to the patient's hip. Once at hand,the template cage is fitted to the 3D bone model and further refined bymanual bending to conform the cage to the 3D bone model. Aftersufficient conformity between the cage and bone model, the cage may beconsidered patient-specific and, post sterilization, is ready forimplantation into the patient.

IMU Kinematic Tracking

Referring to FIG. 154, an exemplary system and process overview isdepicted for kinematic tracking of bones and soft tissues using IMUsthat makes use of a computer and associated software. For example, thiskinematic tracking may provide useful information as to patientkinematics for use in preoperative surgical planning. By way ofexemplary explanation, the instant system and methods will be describedin the context of tracking bone motion and obtaining resulting softtissue motion from 3D virtual models integrating bones and soft tissue.Those skilled in the art should realize that the instant system andmethods are applicable to any bone, soft tissue, or kinematic trackingendeavor. Moreover, while discussing bone and soft tissue kinematictracking in the context of the knee joint or spine, those skilled in theart should understand that the exemplary system and methods areapplicable to joints besides the knee and bones other than vertebrae.

As a prefatory step to discussing the exemplary system and methods foruse with bone and soft tissue kinematic tracking, it is presumed thatthe patient's anatomy (to be tracked) has been imaged (including, butnot limited to, X-ray, CT, MRI, and ultrasound) and virtual 3D models ofthe patient's anatomy have been generated by the software pursuant tothose processes described in the prior “Full Anatomy Reconstruction”section, which is incorporated herein by reference. Consequently, adetailed discussion of utilizing patient images to generate virtual 3Dmodels of the patient's anatomy has been omitted in furtherance ofbrevity.

If soft tissue (e.g., ligaments, tendons, etc) images are availablebased upon the imaging modality, these images are also included andsegmented by the software when the bone(s) is/are segmented to form avirtual 3D model of the patient's anatomy. If soft tissue images areunavailable from the imaging modality, the 3D virtual model of the bonemoves on to a patient-specific soft tissue addition process. Inparticular, a statistical atlas may be utilized for estimating softtissue locations relative to each bone shape of the 3D bone model.

The 3D bone model (whether or not soft tissue is part of the model) issubjected to an automatic landmarking process carried out by thesoftware. The automatic landmarking process utilizes inputs from thestatistical atlas (e.g., regions likely to contain a specific landmark)and local geometrical analyses to calculate anatomical landmarks foreach instance of anatomy within the statistical atlas as discussedpreviously herein. In those instances where soft tissue is absent fromthe 3D bone model, the anatomical landmarks calculated by the softwarefor the 3D bone model are utilized to provide the most likely locationsof soft tissue, as well as the most likely dimensions of the softtissue, which are both incorporated into the 3D bone model to create aquasi-patient-specific 3D bone and soft tissue model. In eitherinstance, the anatomical landmarks and the 3D bone and soft tissue modelare viewable and manipulatable using a user interface for the software(i.e., software interface).

The software interface is communicatively coupled to a visual displaythat provides information to a user regarding the relative dynamicpositions of the patient's bones and soft tissues that comprise thevirtual bone and soft tissue model. In order to provide this dynamicvisual information, which is updated in real-time as the patient's bonesand soft tissue are repositioned, the software interface is alsocommunicatively coupled to any number of IMUs 1002. These IMUs are fixedrigidly to one or more bones corresponding to the bones of the virtual3D model and track relative rotation of the bones. By way of example,the bones may comprise the tibia and femur in the context of the kneejoint or may comprise one or more vertebrae (e.g., the L1 and L5vertebrae) in the context of the spine. In order to track translation ofthe bones, additional tracking sensors (such as ultra-wide band) areassociated with each IMU (or combined as part of a single device) inorder to register the location of each IMU with respect to thecorresponding bone it is mounted to. In this fashion, by tracking thetracking sensors dynamically in 3D space and knowing the position of thetracking sensors with respect to the IMUS, as well as the position ofeach IMU mounted to a corresponding bone, the system is initially ableto correlate the dynamic motion of the tracking sensors to the dynamicposition of the bones in question. In order to obtain meaningful datafrom the IMUs, the patient's bones need to be registered with respect tothe virtual 3D bone and soft tissue model. In order to accomplish this,the patient's joint or bone is held stationary in a predeterminedposition that corresponds with a position of the virtual 3D bone model.For instance, the patient's femur and tibia may be straightened so thatthe lower leg is in line with the upper leg while the 3D virtual bonemodel also embodies a position where the femur and tibia arelongitudinally aligned. Likewise, the patient's femur and tibia may beoriented perpendicular to one another and held in this position whilethe 3D virtual bone and soft tissue model is oriented to have the femurand tibia perpendicular to one another. Using the UWB tracking sensors,the position of the bones with respect to one another is registered withrespect to the virtual 3D bone and soft tissue model, as are the IMUs. Ishould be noted that, in accordance with the foregoing disclosure, theIMUs are calibrated prior to registration using the exemplarycalibration tool 1000 disclosed previously herein.

For instance, in the context of a knee joint where the 3D virtual boneand soft tissue model includes the femur, tibia, and associated softtissues of the knee joint, the 3D virtual model may take on a positionwhere the femur and tibia lie along a common axis (i.e., common axispose). In order to register the patient to this common axis pose, thepatient is outfitted with the IMUs and tracking sensors (rigidly fixedto the tibia and femur) and assumes a straight leg position that resultsin the femur and tibia being aligned along a common axis. This positionis kept until the software interface confirms that the position of theIMUs and sensors is relatively unchanged and a user of the softwareinterface indicates that the registration pose is being assumed. Thisprocess may be repeated for other poses in order to register the 3Dvirtual model with the IMUs and tracking sensors. Those skilled in theart will understand that the precision of the registration willgenerally be increased as the number of registration poses increases.

Referring to FIGS. 175 and 176, in the context of the spine where the 3Dvirtual model includes certain vertebrae of the spine, the 3D virtualmodel may take on a position where the vertebrae lie along a common axis(i.e., common axis pose) in the case of a patient lying flat on a tableor standing upright. In order to register the patient to this commonaxis pose, the patient is outfitted with the IMUs 1002 and othertracking sensors rigidly fixed in position with respect to the L1 and L5vertebrae as depicted in FIG. 175, and assumes a neutral upstandingspinal position that correlates with a neutral upstanding spinalposition of the 3D virtual model. This position is kept until thesoftware interface confirms that the position of the IMUs and trackingsensors is relatively unchanged and a user of the software interfaceindicates that the registration pose is being assumed. This process maybe repeated for other poses in order to register the 3D virtual modelwith the IMUs and tracking sensors. Those skilled in the art willunderstand that the precision of the registration will generally beincreased as the number of registration poses increases.

After registration, the patient anatomy may be moved in 3D space anddynamically tracked using the IMUs and tracking sensors so that themovement of the bones and soft tissue appears graphically on the visualdisplay by way of movement of the 3D virtual model (see FIG. 176 in thecontext of the spine). While the patient moves, the software readsoutputs from the IMUs and/or tracking sensors and processes theseoutputs to convert the outputs into dynamic graphical changes in the 3Dmodel being depicted on the visual display (while keeping track ofligament length, joint pose and articulating surface contact areas, forexample). As shown in FIG. 177, when two or more IMUs are utilized totrack a patient anatomy (e.g., a bone), the software interfacedetermines the relative orientation of a first IMU with respect to asecond IMU as discussed previously herein as each IMU processor isprogrammed to utilize a sequential Monte Carlo method (SMC) with vonMises-Fisher density algorithm to calculate changes in position of theIMU 1002 based upon inputs from the IMU's gyroscopes, accelerometers,and magnetometers. The previous discussion of the SMC method isincorporated herein by reference.

The motion profile of healthy and pathological lumbar patients differsignificantly, such that the out of plane motion is higher forpathological patients. Specifically, healthy and pathological can bedifferentiated using IMUs by having the patient perform threeactivities—axial rotation (AR), lateral bending (LB) andflexion-extension (FE). The coefficients for each of the prescribedmotions are calculated as:

$C_{FE} = \frac{A_{AR} + A_{LB}}{A_{FE}}$$C_{LB} = \frac{A_{AR} + A_{FE}}{A_{LB}}$$C_{AR} = \frac{A_{LB} + A_{FE}}{A_{AR}}$

where A_(M) represents the sum of the absolute value of angular motion,during motion M, for which C is calculated. FIG. 178 depicts theresponse of healthy versus pathological patients as measured using thedual IMUs. By using IMUs, the exemplary system allows patient kinematicanalysis and quantitative evaluation without the need for more expensiveand intrusive tracking systems.

FIGS. 155 and 174 depict an exemplary visual display (i.e., userinterface) operatively coupled to the software interface. As depicted inexemplary form in FIG. 155, a distal femur is shown interfacing with aproximal tibia (and also shown in a phantom proximal fibula). The visualdisplay reflects the software interface's dynamic updating to show howpositions of the respective bones are changing in real-time as thepatient's lower leg is repositioned with respect to the upper leg. Inthe context of FIG. 174, the software is also able to calculatepredicted load distribution upon the proximal tibia based upon kinematicdata. In other words, in the context of a knee joint, the softwaretracks the movement of the distal femur and proximal tibia and recordsthe frequency by which certain portions of the tibia surface arecontacted by the distal femur through a range of motion of the kneejoint. Based upon the frequency of contact between areas of the femurand tibia, the software is operative to generate color gradientsreflective of the contact distribution so that areas in darker red arecontacted the most frequent, whereas areas in blue are contacted theleast, with gradients of shades between red and blue (including orange,yellow, green, and aqua) indicating areas of contact between the mostand least frequent. By way of further example, the software interfacealso highlights locations of soft tissue deformity as well as trackinganatomical axes through this range of motion, such as those shown inFIGS. 160-162.

For example, as shown in FIGS. 156-158, the software utilizes thelocation of soft tissue attachment sites stored in the statistical boneatlas to approximate the attachment sites and, based upon the kinematicmovements of the tracked bones (in this case a femur and tibia),incorporates soft tissue data as part of the virtual models. Morespecifically, the software interface is communicatively coupled to akinematic database and an anatomical database (e.g., a statistical boneatlas). Data from the two databases having been previously correlated(to link kinematic motion of bones with respect to one another with thelocations of soft tissue attachment sites) allows the software toconcurrently display anatomical data and kinematic data. Accordingly,the software is operative to include a ligament construction orreconstruction feature, as shown in FIG. 159, so that ligaments may beshown coupled to the bones. Likewise, the software interface tracks andrecords the motion of the bone and ligament model to show how theligaments are stretched dynamically as the patient's bones are movedthrough a range of motion in a time lapsed sense as shown in FIG. 160.This range of motion data provides clearer images in comparison tofluoroscopy and also avoids subjecting the patient is harmful radiation.

Referencing FIGS. 164-172, the visual representation of the 3D virtualbone and soft tissue model moving dynamically has particularapplicability for a clinician performing diagnosis and pre-operativeplanning. For instance, the clinician may perform various tests on aknee joint, such as the drawer test, to view movement of the bone andsoft tissue across a range of motion. This kinematic trackinginformation may be imported into a surgical planning interface, forexample, to restrict resection plans that may violate the ligamentlengths obtained from the kinematic data. Kinematic data may also beused for real time quantification of various knee tests (e.g., Oxfordknee score) or for the creation of novel quantifiable knee scoringsystems using statistical pattern recognition or machine learningtechniques. In sum, the clinician testing may be used for more accuratepre-operative and post-operative evaluations when alternatives, such asfluoroscopy, may be more costly and more detrimental to patientwellness.

Referring to FIG. 173, an exemplary IMU holster is depicted. The holsteris fixedly mounted to a pair of ratchet straps. The ratchet straps areconfigured to circumscribe the anatomy in question, such as a distalfemur, and be cinched down to inhibit significant repositioning of theholster with respect to the anatomy in question. The holster alsoincludes a IMU package well that is sized to receive an IMU package.When the IMU package is positioned within the well, the well isdimensioned to disallow significant movement of the IMU package withrespect to the holster when a repositionable lock engages the opposingend of the IMU package. In this fashion, the IMU package can be fixed tothe holster or removed from the holster by manipulating the lock.

In exemplary form, the IMU package includes at least one IMU 1002 and anassociated power supply, IMU processor, and a wireless transmitter, inaddition to a power on-off switch. In this fashion. The IMU package is aself-contained item that is able to be coupled to the holster when inuse to track a patient's bone(s) and then removed from the holster. Inthe context of reuse and sterilization, the IMU holster may be reusableor disposable, while the IMU package is intended for re-use.Nevertheless, in certain instances, it may be more economical for theIMU package to be disposable.

In addition to pre-operative and post-operative evaluation, the instantsystem and methods may be useful for intraoperative evaluations. For thepatient-specific resection plan, a custom cutting guide is created fromthe plan and the patient bone data.

Surgical Navigation Using IMUs for TKA

Referring to FIG. 179, an alternate exemplary system and process aredepicted for using one or more inertial measurement units (IMUs) tofacilitate surgical navigation to accurately position a tibial componentduring a total knee arthroplasty (TKA) procedure. The initial steps ofutilizing patient images (whether X-ray, CT, MM, etc.) and performingsegmentation or registration to arrive at virtual templates of thepatient's anatomy and appropriate implant size, shape, and placementparallels that previously described with reference to FIGS. 87, 88,90-92. What differs somewhat are the modules and processes utilizeddownstream from the virtual templating module.

Downstream from the virtual templating module is an initialization modelgeneration module. Similar to the previously discussed jig generationmodule, this module also receives template data and associated planningparameters (i.e., the shape and placement of a patient-specific tibialimplant is known with respect to the patient's residual tibia, as wellas the shape and placement of a patient-specific femoral implant withrespect to the patient's residual femur). Using this patient-specificinformation, the initialization model generation module fabricates a 3Dvirtual model of an initialization device for the patient's nativedistal femur and a 3D virtual model of an initialization device for theproximal tibia. In other words, the 3D model of the femoralinitialization device is created as a “negative” of a particularanatomical surface of the patient's distal femur so that the tangibleinitialization device precisely matches the patient's distal femur.Similarly, the 3D model of the tibial initialization device is createdas a “negative” of the anatomical surface of the patient's proximaltibia so that the tangible initialization device precisely matches thepatient's residual tibia at only a single location and singleorientation. In addition to generating these initialization devices, theinitialization model generation module also generates machine codesnecessary for a rapid prototyping machine, CNC machine, or similardevice to fabricate the tangible femoral initialization device andtibial initialization device. The tangible femoral initialization deviceand tibial initialization device are fabricated and mounted to (orformed concurrently or integrally with) or integral with surgicalnavigation tools configured to have at least one IMU 1002.

Each IMU 1002 is capable of reporting orientation and translational dataand are combined with (e.g., mounted to) one or more surgical tools toassist in surgical navigation to place the femoral component and thetibial component during a TKA procedure. Each IMU 1002 iscommunicatively coupled (wired or wireless) to a software system thatreceives output data from the IMU indicating relative velocity and timethat allows the software to calculate the IMU's current position andorientation, or the IMU 1002 calculates and sends the position andorientation of the surgical instrument, which will be discussed in moredetail hereafter, the position and orientation of the surgicalinstrument associated with the IMU. In this exemplary description, eachIMU 1002 includes three gyroscopes, three accelerometers, and threeHall-effect magnetometers (set of three, tri-axial gyroscopes,accelerometers, magnetometers) that may be integrated into a singlecircuit board or comprised of separate boards of one or more sensors(e.g, gyroscope, accelerometer, magnetometer) in order to output dataconcerning three directions perpendicular to one another (e.g., X, Y, Zdirections). In this manner, each IMU 1002 is operative to generate 21voltage or numerical outputs from the three gyroscopes, threeaccelerometers, and three Hall-effect magnetometers. In exemplary form,each IMU 1002 includes a sensor board and a processing board, with asensor board including an integrated sensing module consisting of athree accelerometers, three gyroscopic sensors and three magnetometers(LSM9DS, ST-Microelectronics) and two integrated sensing modulesconsisting of three accelerometers, and three magnetometers (LSM303,ST-Microelectronics). In particular, the IMUs 1002 each include angularmomentum sensors measuring rotational changes in space for at leastthree axes: pitch (up and down), yaw (left and right) and roll(clockwise or counter-clockwise rotation). More specifically, eachintegrated sensing module consisting magnetometer is positioned at adifferent location on the circuit board, with each magnetometer assignedto output a voltage proportional to the applied magnetic field and alsosense polarity direction of a magnetic field at a point in space foreach of the three directions within a three dimensional coordinatesystem. For example, the first magnetometer outputs voltage proportionalto the applied magnetic field and polarity direction of the magneticfield in the X-direction, Y-direction, and Z-direction at a firstlocation, while the second magnetometer outputs voltage proportional tothe applied magnetic field and polarity direction of the magnetic fieldin the X-direction, Y-direction, and Z-direction at a second location,and the third magnetometer outputs voltage proportional to the appliedmagnetic field and polarity direction of the magnetic field in theX-direction, Y-direction, and Z-direction at a third location. By usingthese three sets of magnetometers, the heading orientation of the IMUmay be determined in addition to detection of local magnetic fieldfluctuation. Each magnetometer uses the magnetic field as reference anddetermines the orientation deviation from magnetic north. But the localmagnetic field can, however, be distorted by ferrous or magneticmaterial, commonly referred to as hard and soft iron distortion. Softiron distortion examples are materials that have low magneticpermeability, such as carbon steel, stainless steel, etc. Hard irondistortion is caused by permanent magnets. These distortions create anon-uniform field (see FIG. 182), which affects the accuracy of thealgorithm used to process the magnetometer outputs and resolve theheading orientation. Consequently, as discuss in more detail hereafter,a calibration algorithm is utilized to calibrate the magnetometers torestore uniformity in the detected magnetic field. Each IMU 1002 may bepowered by a replaceable or rechargeable energy storage device such as,without limitation, a CR2032 coin cell battery and a 200 mAhrechargeable Li ion battery.

The integrated sensing modules in IMU 1002 may include a configurablesignal conditioning circuit and analog to digital converter (ADC), whichproduces the numerical outputs for the sensors. The IMU 1002 may usesensors with voltage outputs, where an external signal conditioningcircuit, which may be an offset amplifier that is configured tocondition sensor outputs to an input range of a multi-channel 24 bitanalog-to-digital converter (ADC) (ADS1258, Texas Instrument). The IMU1002 further includes an integrated processing module that includes amicrocontroller and a wireless transmitting module (CC2541, TexasInstrument). Alternatively, the IMU 1002 may use separate low powermicrocontroller (MSP430F2274, Texas Instrument) as the processor and acompact wireless transmitting module (A2500R24A, Anaren) forcommunication. The processor may be integrated as part of each IMU 1002or separate from each IMU, but communicatively coupled thereto. Thisprocessor may be Bluetooth compatible and provide for wired or wirelesscommunication with respect to the gyroscopes, accelerometers, andmagnetometers, as well as provide for wired or wireless communicationbetween the processor and a signal receiver.

Each IMU 1002 is communicatively coupled to a signal receiver, whichuses a pre-determined device identification number to process thereceived data from multiple IMUs. The data rate is approximately 100 Hzfor a single IMU and decreases as more IMUs join the shared network. Thesoftware of the signal receiver receives signals from the IMUs 1002 inreal-time and continually calculates the IMU's current position basedupon the received IMU data. Specifically, the acceleration measurementsoutput from the IMU are integrated with respect to time to calculate thecurrent velocity of the IMU in each of the three axes. The calculatedvelocity for each axis is integrated over time to calculate the currentposition. But in order to obtain useful positional data, a frame ofreference must be established, which includes calibrating each IMU.

Prior to utilizing the IMUs 1002 for surgical navigation, the IMUs arecalibrated pursuant to the calibration disclosure previously discussedherein and consequently incorporated herein by reference. Moreover, eachIMU processor is programmed to utilize a sequential Monte Carlo method(SMC) with von Mises-Fisher density algorithm to calculate changes inposition of the IMU 1002 based upon inputs from the IMU's gyroscopes,accelerometers, and magnetometers.

Subsequent to calibration, as shown in FIG. 179, the IMUs 1002 may beregistered to the anatomy in question. In this case, the IMUs areregistered to the proximal tibia and the distal femur. In order toregister the IMUs 1002 to the proximal tibia, a first IMU is mounted toa proximal tibia positioning tool having an interior surface thatmatches the exterior of a portion of the proximal tibia in only a singlelocation and orientation. Once positioned in this unique location andorientation, the proximal tibia positioning tool is mounted to theproximal tibia, in exemplary form using surgical screws. A second IMU isfixedly mounted to a rotational navigation tool, which is positioned ontop of a resected proximal tibia. When the rotational navigation tool iscorrectly oriented and rotationally positioned on the patient's proximalresected tibia, the orientation of the second IMU 1002 relative to thefirst IMU is known. An operator indicates to the software system thatfirst IMU is in its correct position and then the software uses theoutputs from both IMUs to establish the position of the second IMU. Thisposition of the second IMU is compared to a previously determinedsurgical plan to determine if the orientation and rotational alignmentof the rotational navigation tool is correct with respect to thesurgical plan. If so, the rotational navigation tool is utilized todrill one or more holes into the proximal tibia for later alignment ofthe permanent tibial component of the TKA. If the rotational alignmentis awry, the software and visual display provides feedback to thesurgeon to facilitate proper surgical navigation of the navigationaltool with respect to the proximal tibia.

In exemplary form, the software program provides a graphical userinterface for a surgeon that displays virtual models of the patient'sproximal tibia and a virtual model of the rotational navigation tool(the virtual model of the patient's tibia having already been completedpursuant to the virtual templating step, and the virtual model of therotational navigation tool having been previously loaded into the systemfor the particular rotational navigation tool that may be utilized), andupdates the orientation of the tibia and rotational navigation tool inreal time via the graphical user interface providing position andorientation information to the surgeon. Rather than using a graphicaluser interface, the instant system may include surgical devices havingindicator lights indicating to the surgeon whether the rotationalnavigation tool is correctly oriented and, if not, what direction(s) therotational navigation tool needs to be repositioned to correctly orientthe navigation tool consistent with the pre-operative planning. Afterorientation and location of the rotational navigation tool have beenachieved, the surgeon may drill one or more holes into the proximalfemur in preparation of implanting the proximal tibial component of theTKA. An analogous rotational navigation tool and set of IMUs may beused, along with an analogous process for registration, with thesoftware system to assist with placement of the distal femoral componentduring the TKA.

Those skilled in the art are familiar with conventional mandible boneplates and, accordingly, a detailed discussion of general designs ofmandible bone plates has been omitted in furtherance of brevity. Whatthe present system and methods accomplish, unlike conventional systemsand methods, is the formation of patient-specific bone plates andplacement guides that account for the shape of both the residual boneand the bone graft. In particular, for each bone plate locationidentified (either automatically or manually), the system designed avirtual 3D bone plate and associated placement guide. Each virtual 3Dbone plate and guide model is overlaid with respect to the hybrid 3Dmodel (including bone graft and patient residual bone in theirreconstructed location) to ensure the underside of each virtual 3D boneplate and guide model is the negative of the underlying bone, whetherthat comprises the bone graft or the residual bone. In this manner, thevirtual 3D bone plate and guide model work together to ensure properplacement of the bone plate and corresponding engagement between thebone plate, bone graft, and residual bone. Exemplary mounting techniquesfor securing a bone plate to a bone graft and residual bone may include,without limitation, screws, dowels, and pins. In order to accommodateone or more of these mounting techniques or others, each virtual 3D boneplate and placement guide includes one or more through orifices. Afterthe design of each virtual 3D bone plate and guide is completed, thesystem generates and outputs machine code necessary for a rapidprototyping machine, CNC machine, or similar device to fabricate each 3Dbone plate and guide, which is followed by fabrication of the actualbone plate and guide.

UWB and IMU Hybrid Tracking System

Referring to FIGS. 189-212, an exemplary hybrid navigation and trackingsystem is disclosed. This exemplary hybrid system makes use of ultrawide band (UWB) and inertial measurement units (IMUS) and comprises atleast one central unit (i.e., a core unit) and one peripheral unit(i.e., a satellite unit). Each central unit comprises, in exemplaryform, at least one microcomputer, at least one tri-axial accelerometer,at least one tri-axial gyroscope, at least three tri-axialmagnetometers, at least one communication module, at least one UWBtransceiver, at least one multiplexer, and at least four UWB antennas(see FIG. 189) Also, each peripheral unit comprises, in exemplary form,at least one microcomputer, at least one tri-axial accelerometer, atleast one tri-axial gyroscope, at least three tri-axial magnetometers,at least one communication module, at least one UWB transceiver, atleast one multiplexer, and at least four UWB antennas.

As shown in FIGS. 190A and 190B, this exemplary system making use of thehybrid UWB and IMU surgical navigation system uses the central unit as apositional reference, and navigate the relative translations andorientations of the surgical instrument using the peripheral unit.

One of the important aspects of using an UWB navigation system for highaccuracy surgical navigation is to account for antenna phase centervariation at the transmitters and receivers. Ideally all frequenciescontained in the pulse are radiated from the same point of the UWBantenna and, thus, would have a fixed phase center. In practice, thephase center varies with both frequency and direction. UWB antenna phasecenters can vary by up to 3 centimeters as the angle of arrival isvaried.

In order to mitigate antenna phase center error, each UWB antenna shouldhave its phase center precisely characterized at all possible angles ofarrival over the entire operational frequency band. Phase centercharacterization and mitigation is routinely performed in GPS systems toimprove location accuracy. UWB tags and anchors can utilize a variety ofUWB antennas including monopoles, dipoles, spiral slots, and Vivaldis.

FIGS. 153 and 154 outline how a UWB antenna phase center can becharacterized in 3-D so that the phase center bias can subsequently beremoved during system operation. As shown in FIG. 191, the UWB antennais placed in an anechoic chamber to quantify how the phase center isaffected by the directivity based on time domain measurements. Two ofthe same UWB antennas are put face to face and separated by a distanceof 1.5 meters. The receiving antenna is rotated around the calculated“apparent phase center” from −45 to 45 degrees at 5 degrees per step.The apparent phase center is tracked on the UWB receiving antenna as itis rotated from −45 to 45 degrees with an optically tracked probe. Theoptical system provides a ground truth reference frame withsub-millimeter accuracy. These reference points from the optical systemare used to calculate the actual center of rotation during theexperiment. This allows changes in the actual phase center as thereceiving antenna is rotated to be separated from physical movement ofthe apparent phase center, illustrated in FIG. 191. FIG. 192 shows anexample of the measured phase center error for a UWB Vivaldi antenna inthe vertical and horizontal directions (E and H cuts). As shown in FIG.192, the measured phase center variation versus rotating angle indicatesthat errors of greater than 1-2 centimeters are possible as the angle ofarrival is varied.

This process is used to characterize the UWB antenna phase centervariation for each UWB antenna design used in the UWB navigation system(e.g., monopole, spiral slot). Once the UWB antenna phase center hasbeen fully characterized in 3-D for all possible angles of arrival, thephase center error can be removed from the system by subtracting out thephase center bias for each tag using the calculated 3-D position of eachtag.

An alternative approach for removing phase center bias is to rigidlyattach the antenna to a motorized gimbal where a digital goniometer orinertial measurement unit can provide the angular feedback to a controlsystem of the motors so that the antenna can be positioned andorientated in its optimal positions.

As shown in FIG. 193, by connecting multiple antennas to a singletransceiver, it enables one to create multiple anchors or tags withinthe same UWB unit. The UWB antenna array in both central and peripheralunits can be arranged in any configuration with the condition that oneof the antennas does not reside on the same plane with the other three.For example, a tetrahedron configuration will satisfy this condition(See FIG. 189).

The UWB antenna array in the central unit serves as the anchors for thesystem. For example, a tetrahedron configuration will have four antennasconnected to a single UWB transceiver. This creates four anchors in thecentral unit. With a single clock, and a single transceiver to feed theUWB pulses into multiple antennas, this configuration enables clocksynchronization among all anchors in the unit. This configuration cantremendously improve the flexibility of the installation of the anchors,as well as easing the calibration procedure of the unit. In a shortrange localization application, a single central system is sufficient toprovide adequate anchors for localization. In a large area localizationapplication, multiple central systems can be used. The clocks of thecentral units are synchronized during operation with either wired orwireless methods.

Referring to FIG. 193, a block diagram of the silicon-germaniummonolithic microwave intergrated circuit (MMIC) based UWB transmitter isdepicted where a cross-coupled oscillator core is transiently turned onby a current spike generated by a Schmitt trigger driving a currentmirror. FIG. 194 depicts an integrated board design with the MIMIC atthe feed point of the UWB antenna. The MIMIC based transmitter is morecompact and only has a load requirement of 6 milliwatts for operation(1.5 volts, 4 milliamps).

The UWB antenna array in the peripheral unit serves as the tags for thesystem. For example, a tetrahedron configuration has four antennasconnected to a single UWB transceiver. This creates four tags in theperipheral unit. With a single clock, and a single transceiver to feedthe UWB pulses into multiple antennas, this configuration enables clocksynchronization among all anchors in the unit. This configurationenables the ability to calculate orientations of a peripheral unit byapplying rigid body mechanics based on the localization of the tags.

Clock jitter and drift should be characterized and removed from theranging signals to achieve sub-centimeter accuracy. FIG. 196 illustratesthe jitter and drift observed in the received range difference signalsfor a tag in a static location over a period of 23 minutes. Significanteffects are observed including errors as high as 30-40 millimeters foreach time difference. This causes 3-D positioning errors of 30millimeters or more. These extremely large errors should be mitigatedfor the system to achieve consistent millimeter 3-D accuracy.

Referring to FIG. 197, localization of the tag is achieved with a TDOAalgorithm, which looks at the relative time differences between theanchors. There are four anchors at known positions R_(x1) or (x₁, y₁,z₁), R_(x2) or (x₂, y₂, z₂), R_(x3) or (x₃, y₃, z₃), and R_(x4) or (x₄,y₄, z₄), and a tag at an unknown position (x_(u), y_(u), z_(u)). Themeasured distance between the four known position receivers and theunknown position tag can be represented as ρ₁, ρ₂, ρ₃, and ρ₄, which isgiven by:

$\begin{matrix}\begin{matrix}{\rho_{i} = {\sqrt{\left( {x_{i} - x_{u}} \right)^{2} + \left( {y_{i} - y_{u}} \right)^{2} + \left( {z_{i} - z_{u}} \right)^{2}} + {ct}_{u}}} \\{= {f\left( {x_{u},y_{u},z_{u},t_{u}} \right)}}\end{matrix} & (1)\end{matrix}$

where i=1, 2, 3, and 4, c is speed of light, and t_(u) is the unknowntime delay in hardware.The differential distances between four anchors and the tag can bewritten as

$\begin{matrix}\begin{matrix}{{\Delta\rho}_{1\; k} = {\rho_{1} - \rho_{k}}} \\{= {\sqrt{\left( {x_{1} - x_{u}} \right)^{2} + \left( {y_{1} - y_{u}} \right)^{2} + \left( {z_{1} - z_{u}} \right)^{2}} -}} \\{\sqrt{\left( {x_{k} - x_{u}} \right)^{2} + \left( {y_{k} - y_{u}} \right)^{2} + \left( {z_{k} - z_{u}} \right)^{2}}}\end{matrix} & (2)\end{matrix}$

where k=2, 3, and 4, and the time delay t_(u) in hardware has beencancelled.Differentiating this equation will give

$\begin{matrix}\begin{matrix}{{d\; {\Delta\rho}_{1\; k}} = {\frac{{\left( {x_{1} - x_{u}} \right){dx}_{u}} + {\left( {y_{1} - y_{u}} \right){dy}_{u}} + {\left( {z_{1} - z_{u}} \right){dz}_{u}}}{\sqrt{\left( {x_{1} - x_{u}} \right)^{2} + \left( {y_{1} - y_{u}} \right)^{2} + \left( {z_{1} - z_{u}} \right)^{2}}} +}} \\{\frac{{\left( {x_{k} - x_{u}} \right){dx}_{u}} + {\left( {y_{k} - y_{u}} \right){dy}_{u}} + {\left( {z_{k} - z_{u}} \right){dz}_{u}}}{\sqrt{\left( {x_{k} - x_{u}} \right)^{2} + \left( {y_{k} - y_{u}} \right)^{2} + \left( {z_{k} - z_{u}} \right)^{2}}}} \\{= {{\left( {\frac{x_{1} - x_{u}}{\rho_{1} - {c\; \tau_{u}}} + \frac{x_{k} - x_{u}}{\rho_{k} - {c\; \tau_{u}}}} \right){dx}_{u}} + {\left( {\frac{y_{1} - y_{u}}{\rho_{1} - {c\; \tau_{u}}} + \frac{y_{k} - y_{u}}{\rho_{k} - {c\; \tau_{u}}}} \right){dy}_{u}} +}} \\{{\left( {\frac{z_{1} - z_{u}}{\rho_{1} - {c\; \tau_{u}}} + \frac{z_{k} - z_{u}}{\rho_{k} - {c\; \tau_{u}}}} \right){dz}_{u}}}\end{matrix} & (3)\end{matrix}$

In equations (3-5), x_(u), y_(u), and z_(u) are treated as known valuesby assuming some initial values for the tag position. dx_(u), dy_(u),and dz_(u) are considered as the only unknowns. From the initial tagposition the first set of dx_(u), dy_(u), and dz_(u) can be calculated.These values are used to modify the tag position x_(u), y_(u), andz_(u). The updated tag position x_(u), y_(u), and z_(u) can beconsidered again as known quantities. The iterative process continuesuntil the absolute values of dx_(u), dy_(u), and dz_(u) are below acertain predetermined threshold given by

ε=√{square root over (dx _(u) ² +dy _(u) ² +dz _(u) ²)}  (4)

The final values of x_(u), y_(u), and z_(u) are the desired tagposition. The matrix form expression of (5) is

$\begin{matrix}{{\begin{bmatrix}{d\; {\Delta\rho}_{12}} \\{d\; {\Delta\rho}_{13}} \\{d\; {\Delta\rho}_{14}}\end{bmatrix} = {\begin{bmatrix}\alpha_{11} & \alpha_{12} & \alpha_{13} \\\alpha_{21} & \alpha_{22} & \alpha_{23} \\\alpha_{31} & \alpha_{32} & \alpha_{33}\end{bmatrix}\begin{bmatrix}{dx}_{u} \\{dy}_{u} \\{dz}_{u}\end{bmatrix}}}{where}} & (5) \\{{\alpha_{{k - 1},1} = {\frac{x_{1} - x_{u}}{\rho_{1} - {c\; \tau_{u}}} + \frac{x_{k} - x_{u}}{\rho_{k} - {c\; \tau_{u}}}}}{\alpha_{{k - 1},2} = {\frac{y_{1} - y_{u}}{\rho_{1} - {c\; \tau_{u}}} + \frac{y_{k} - y_{u}}{\rho_{k} - {c\; \tau_{u}}}}}{\alpha_{{k - 1},3} = {\frac{z_{1} - z_{u}}{\rho_{1} - {c\; \tau_{u}}} + \frac{z_{k} - z_{u}}{\rho_{k} - {c\; \tau_{u}}}}}} & (6)\end{matrix}$

The solution of equation (6) is given by

$\begin{matrix}{\begin{bmatrix}{dx}_{u} \\{dy}_{u} \\{dz}_{u}\end{bmatrix} = {\begin{bmatrix}\alpha_{11} & \alpha_{12} & \alpha_{13} \\\alpha_{21} & \alpha_{22} & \alpha_{23} \\\alpha_{31} & \alpha_{32} & \alpha_{33}\end{bmatrix}^{- 1}\begin{bmatrix}{d\; {\Delta\rho}_{12}} \\{d\; {\Delta\rho}_{13}} \\{d\; {\Delta\rho}_{14}}\end{bmatrix}}} & (7)\end{matrix}$

where [ ]⁻¹ represents the inverse of the α matrix. If there are morethan four anchors, the least-squares approach can be applied to find thetag position.

A proof of concept experiment was conducted to examine the translationtracking of the UWB system with a TDOA algorithm. An experiment was runusing five anchors while tracking a single tag dynamically along a rail.An optical tracking system was used for comparison. The results of theexperiment are shown in FIGS. 160A-160C.

FIG. 199A shows a truncated list of parameters for the line-of-sight(LOS) operating room environment fit to the IEEE 802.15.4a channel model(shown in equation 8), which were obtained with time domain andfrequency domain experimental data.

The operating room is a harsh indoor environment for UWB positioning.FIG. 199(A) shows a truncated list of parameters for the line-of-sight(LOS) operating room environment fit to the IEEE 802.15.4a channel model(shown in equation 8) that were obtained with time domain and frequencydomain experimental data. FIG. 199B shows the pathloss for the operatingroom (OR) environment obtained by fitting experimental data to equation9 and compared to residential LOS, commercial LOS, and industrial LOS.The pathloss in the OR is most similar to residential LOS, although thiscan change depending on which instruments are placed near thetransmitter and receiver or the locations of the UWB tags and anchors inthe room.

$\begin{matrix}{{h(t)} = {\sum\limits_{l = 0}^{L}\; {\sum\limits_{k = 0}^{K}\; {a_{k,l}{\exp \left( {j\; \phi_{k,l}} \right)}{\delta \left( {t - T_{l} - \tau_{k,l}} \right)}}}}} & (8) \\{{{PL}(d)} = {{PL}_{0} + {10\; n\; {\log_{10}\left( \frac{d}{d_{0}} \right)}}}} & (9)\end{matrix}$

where equation 8 is the impulse response of the UWB channel in the timedomain, and equation 9 is the pathloss model used in the correspondingUWB channel.

The orientations of the units can be estimated by using four tagsattached rigidly on the same body. Given four set of pointsZ={P1,P2,P3,P4}, which are moving as a single, whole rigid body relativeto the anchors. The relative change in orientations between the tags andanchors can be calculated by minimizing the following equation,

$\begin{matrix}{\sum\limits_{1}^{4}\; {{Z_{i} - {T*Z_{n}}}}} & (10)\end{matrix}$

where Z_(i)=Z*T_(i), with T_(i) being the initial orientations of thetags relative to the anchors, T is the new orientation to be calculated,and Zn is the new location of the points.

Apart from the localization capability, UWB can also significantlyimprove the wireless communication of the surgical navigation system.Preexising surgical navigation systems utilizing wireless technology aretypically confined within the 400 MHz, 900 MHz, and 2.5 GHz Industrial,Scientific, and Medical (ISM) band. The landscape of these bands areheavily polluted due to many other devices sharing the same band.Secondly, although the data rate in these bands vary with the protocol,it is becoming impossible to handle the increasing demand of larger datasets necessary for navigation systems. UWB technology can also serve asa communication device for the surgical navigation system. It operatesin a relatively clean bandwidth and it has several folds higher datarate than the conventional wireless transmission protocol. In addition,the power consumption of UWB communication is similar to Bluetooth lowenergy (BLE).

Turning to the inertial navigation system of the present disclosure,this inertial navigation system uses the outputs from a combination ofaccelerometers, gyroscopes, and magnetometers to determine thetranslations and orientations of the unit. For translation navigation,the accelerometer provides linear accelerations experienced by thesystem. The translations of the system can be navigated using the deadreckoning method. Using the equation of motion, the basic calculationfor position from the accelerometer data is to integrate accelerationover time twice as shown below,

ν=∫aΔt=ν _(i) +aΔt  (11)

s=∫νΔt=s _(i)+ν_(i) Δt+½aΔt ²  (12)

where a is acceleration, ν is velocity, ν_(i) is velocity of theprevious state, s is position, s_(i) is position from the previousstate, and Δt is time interval.

Upon close examination, one will notice that the velocity and positionfrom the previous states also contributes the calculation of the currentstates. In other words, if there is any noise and error from theprevious states, it will be accumulated. This is known as the arithmeticdrift error. A difficult part of designing the inertial navigationsystem is the ability to control and minimize this drift. In the presentcase, this drift is controlled by the UWB system, which is described inmore detail hereafter.

For orientation navigation, a multitude of estimation and correctionalgorithms (e.g. Kalman filters, particle filters) can be used toperform sensor fusion. The fundamental of sensor fusion with an inertialdevice is to use gyroscopes to estimate the subsequent orientations ofthe unit and, at the same time, uses accelerometers and magnetometers tocorrect the error from a previous estimation. Different algorithmscontrol the error correction in different ways. With a Kalman filter,the system is assumed to be linear and Gaussian, while no suchassumption is made with a particle filter.

The basic Kalman filter can be separated into 2 major sets of equations,which are the time update equations and the measurement updateequations. The time update equations predict the priori estimates attime k with the knowledge of the current states and error covariance attime k−1 in equation (13) respectively.

x _(k) =Ax _(k-1) Bu _(k-1) +w _(k-1)  (13)

P _(k) ⁻ =AP _(k-1) A ^(T) +Q  (14)

where x_(k) is the state vector of the current state, x_(k-1) is thestate vector from the previous state, A is the transitional matrix modelto transform the previous state into the current state, B is the matrixmodel for controlled input u_(k-1) from the previous state, and w_(k-1)is the process noise, which is independent and normally distributedaround zero means with process noise covariance matrix Q.

The measurements update equations use the measurements acquired with thepriori estimates to calculate the posteriori estimates.

S _(k) =HP _(k) ⁻ H ^(T) R  (15)

K _(k) =P _(k) ⁻ H _(k) ^(T) S _(k) ⁻¹  (16)

{circumflex over (x)} _(k) ={circumflex over (x)} _(k) ⁻ +K _(k) {tildeover (y)} _(k) ,{tilde over (y)} _(k) =z _(k) −H{circumflex over (x)}_(k) ⁻  (17)

P _(k)−(I−K _(k) H _(k))P _(k) ⁻  (18)

where P_(k) ⁻ is the priori error covariance matrix, P_(k) is the priorierror covariance matrix, S_(k) is the innovation error covariancematrix, H is the priori prediction, {circumflex over (x)}_(k), is theposteriori state estimate, and {circumflex over (x)}_(k) ⁻ is the prioriestimate, K_(k) is the optimal Kalman gain, z_(k) is the measurement.

The posteriori estimate is then use to predict priori estimate at thenext time step. As displayed from the equations above, no furtherinformation is required beside the state and error covariance from theprevious state. The algorithm is extremely efficient and suitable forthe navigation problem where multiple concurrent input measurements arerequired.

There are multiple different implementations of a Kalman filter thattackles the linear and Gaussian assumptions such as an extended Kalmanfilter that linearize the system, as well as Sigma point and UnscentedKalman filters that provide non-linear transformation of the system.

The fundamental of the particle filter (PF) or Sequential Monte Carlo(SMC) filter is solving a probabilistic model that computes theposterior probability density function of an unknown process and uses itin the estimation calculation. It generally involves two-stage processesof state prediction and state update to resolve the posterior density.Using a particle filter can be considered a brute force approach toapproximate the posterior density with a large sum of independent andidentically distributed random variables or particles from the sameprobability density space.

Consider a set of N independent random samples are drawn from aprobability density p(x_(k)|z_(k)),

x _(x)(i)˜p(x _(k) |z _(1:k)), i=1:N  (19)

The Monte Carlo representation of the probability density can then beapproximated as,

$\begin{matrix}{{p\left( x_{k} \middle| z_{1\text{:}\mspace{14mu} k} \right)} \approx {\frac{1}{N}{\sum\limits_{i = 1}^{N}\; {\delta_{x_{k}{(i)}}\left( x_{k} \right)}}}} & (20)\end{matrix}$

where δ_(x(i)) is the Dirac delta function of the points mass.

Using this interpretation, the expectation of the any testing functionh(x) is given by

$\begin{matrix}{\begin{matrix}{{\left( {h\left( x_{k} \right)} \right)} = {\int{{h\left( x_{k} \right)}{p\left( x_{k} \middle| z_{1\text{:}\mspace{14mu} k} \right)}{dx}_{k}}}} \\{\approx {\int{{h\left( x_{k} \right)}\frac{1}{N}{\sum\limits_{i = 1}^{N}\; {{\delta_{x_{k}{(i)}}\left( x_{k} \right)}{dx}_{k}}}}}} \\{{= {\frac{1}{N}{\sum\limits_{i = 1}^{N}\; {h\left( {x_{k}(i)} \right)}}}},\mspace{14mu} {i = {1\text{:}\mspace{14mu} N}}}\end{matrix}\mspace{14mu}} & (21)\end{matrix}$

In practice, sampling from p(x) directly is usually not possible due tolatent hidden variables in the estimation. Alternatively, samples aredrawn from a different probability density q(x_(k)|z_(1:k)) is proposed,

x _(k)(i)˜q(x _(k) |z _(1:k)), i=1:N  (22)

which is generally known as the importance function or the importancedensity. A correction step is then used to ensure the expectationestimation from the probability density q(x_(k)|z_(1:k)) remains valid.The correction factor, which is generally regarded as the importanceweights of the samples (w_(k)(i)), is proportional to the ratio betweenthe target probability density and the proposed probability density,

$\begin{matrix}{{{w_{k}(i)} \propto {\frac{p\left( x_{k} \middle| z_{1:\mspace{14mu} k} \right)}{q\left( x_{k} \middle| z_{1:\mspace{14mu} k} \right)}\mspace{14mu} i}} = {1\text{:}\mspace{14mu} N}} & (23)\end{matrix}$

The importance weights are normalized,

Σ_(i=1) ^(N) w _(k)(i)=1  (24)

Based on the sample drawn from equation (22), the posterior probabilitydensity becomes,

$\begin{matrix}\begin{matrix}{{p\left( x_{k} \middle| z_{1\text{:}\mspace{14mu} k} \right)} = \frac{{p\left( z_{k} \middle| x_{k} \middle| z_{k - 1} \right)}{p\left( x_{k} \middle| z_{k - 1} \right)}}{p\left( z_{k} \middle| z_{k - 1} \right)}} \\{= {{\frac{{p\left( z_{k} \middle| x_{k} \right)}{p\left( x_{k} \middle| x_{k - 1} \right)}}{p\left( z_{k} \middle| z_{k - 1} \right)}{p\left( x_{k} \middle| z_{{1\text{:}\mspace{14mu} k} - 1} \right)}} \propto (26)}} \\{{{p\left( z_{k} \middle| x_{k} \right)}{p\left( x_{k} \middle| x_{k - 1} \right)}{p\left( x_{k} \middle| z_{{1\text{:}\mspace{14mu} k} - 1} \right)}(27)}}\end{matrix} & (25)\end{matrix}$

And the importance weight from equation (22)(23) becomes,

$\begin{matrix}{{{w_{k}(i)} \propto \frac{{p\left( z_{k} \middle| {x_{k}(i)} \right)}{p\left( {x_{k}(i)} \middle| {x_{k - 1}(i)} \right)}{p\left( {x_{{1\text{:}\mspace{14mu} k} - 1}(i)} \middle| z_{{1\text{:}\mspace{14mu} k} - 1} \right)}}{{q\left( {x_{k}(i)} \middle| {x_{{1\text{:}\mspace{14mu} k} - 1}(i)} \right)}{q\left( {x_{{1\text{:}\mspace{14mu} k} - 1}(i)} \middle| z_{{1\text{:}\mspace{14mu} k} - 1} \right)}}},{i = {1\text{:}\mspace{14mu} N}}} & (28) \\{= {{w_{k - 1}(i)}\frac{{p\left( z_{k} \middle| {x_{k}(i)} \right)}{p\left( {x_{k}(i)} \middle| {x_{k - 1}(i)} \right)}}{q\left( {x_{k}(i)} \middle| {x_{{1\text{:}\mspace{14mu} k} - 1}(i)} \right)}}} & (29) \\{\propto {{w_{k - 1}(i)}\frac{{p\left( z_{k} \middle| {x_{k}(i)} \right)}{p\left( {x_{k}(i)} \middle| {x_{k - 1}(i)} \right)}}{q\left( {x_{k}(i)} \middle| {x_{k - 1}(i)} \right)}}} & (30)\end{matrix}$

The posterior probability density can then be approximated empiricallyby,

p(x _(k) |z _(1:k))≈Σ_(i=1) ^(N) w _(k)(i)δ_(x) _(k) _((i))(x_(k))  (31)

The expectation of the estimation from equation (20) can be expressedas,

$\begin{matrix}\begin{matrix}{{\left( {h\left( x_{k} \right)} \right)} = {\int{{h\left( x_{k} \right)}{p\left( x_{k} \middle| z_{1\text{:}\mspace{14mu} k} \right)}{dx}_{k}}}} \\{\approx {\int{{h\left( x_{k} \right)}{\sum\limits_{i = 1}^{N}\; {{w_{k}(i)}{\delta_{x_{k}{(i)}}\left( x_{k} \right)}}}}}} \\{{= {\sum\limits_{i = 1}^{N}\; {{w_{k}(i)}{h\left( {x_{k}(i)} \right)}}}},\mspace{14mu} {i = {1\text{:}\mspace{14mu} N}}}\end{matrix} & (32)\end{matrix}$

The technique demonstrated by equations (28-31) is regarded as thesequential importance sampling (SIS) procedure. However, the issue withSIS is that the importance weights will be concentrated on a few sampleswhile the remainder of the samples become negligible after a fewrecursions. This is known as the degeneracy problem with a particlefilter. A frequent approach to counter this problem is resampling thesamples so that they are all equally weighted based on the posteriordensity. However, since resampling the samples introduces Monte Carloerror, resampling may not be performed in every recursion. It shouldonly be executed when the distribution of the importance weight of thesample has been degraded. The state of the samples is determined by theeffective sample size, which is defined by,

$\begin{matrix}{{N_{eff} = \frac{N}{1 + {{var}\left( {w_{k}^{*}(i)} \right)}}},\mspace{14mu} {i = {1\text{:}\mspace{14mu} N}}} & (33)\end{matrix}$

where w_(k)*(i) is the true weight of the sample,

$\begin{matrix}{{{w_{k}^{*}(i)} = \frac{p\left( x_{k} \middle| z_{1\text{:}\mspace{14mu} k} \right)}{q\left( {x_{k}(i)} \middle| {x_{k - 1}(i)} \right)}},\mspace{14mu} {i = {1\text{:}\mspace{14mu} N}}} & (34)\end{matrix}$

However, as the true weight of the sample cannot be determined directly,the following method is used to approximate the effective sample sizeempirically with the normalized weights.

$\begin{matrix}{{N_{eff} = \frac{1}{\sum\limits_{i}^{N}\; w_{i}^{2}}},\mspace{14mu} {i = {1\text{:}\mspace{14mu} N}}} & (35)\end{matrix}$

Resampling is performed when N_(eff) drops below a predeterminedthreshold N_(th), which is done by relocating the samples with smallweight to the samples with higher weights, hence, redistributing theweights of the particles.

One of the challenges of using an inertial navigation system is that itis sensitive to ferromagnetic and martensitic materials (e.g. Carbonsteel), as well as permanent magnets (collectively, “magneticmaterials”), which are commonly used materials in surgicalinstrumentation, as well as high power equipment. As part of the presentsystem, the inertial system component uses a minimum of threemagnetometers for detecting anomalies in the magnetic field. Thesemagnetometers are placed in different locations in the unit. The outputsof the magnetometers change differently as an object composed ofmagnetic materials move into the vicinity of the unit. A detectionalgorithm is implemented to detect subtle changes among eachmagnetometer's output. Once calibrated, it is expected that theinstantaneous magnitude of absolute difference of any two signalvectors, M₁, M₂, M₃, signals is near zero and each has instantaneousmagnitude of approximately one. Thus, in the case of calibratedmagnetometers and no added distortion, the relationships in FIG. 200should hold true.

Using the information in FIG. 200, the outlier values in the signalformed by the following equations can be detected. First, an inversenormal probability density function may be used to weight the magnitudeof the received magnetometer signal. More specifically, the inversenormal probability density function is used to weight the magnitudedifference of the on board magnetometers, from which outliers can beconsidered distorted samples. When distortion is detected, UWBorientation can be used as a substitute as discussed hereafter.

Referencing FIG. 201, a block diagram of determining the unit'stranslation and orientations is depicted. The exemplary hybrid inertialnavigation and UWB system utilizes the advantages of each of thesubsystems (i.e., IMU, UWB) to achieve subcentimeter accuracy intranslation and subdegree in orientation. Estimation and correctionalgorithms (e.g., Kalman filter or particle filter) can be used todetermine translations and orientations of the system. The linearacceleration from the inertial navigation system provides good estimatesas to the translations of the system, while the UWB localization systemprovides a correction to transform the estimates into accuratetranslation data. For orientation, the inertial tracking system issufficient to provide accurate orientations during normal operation. Theorientation data from the UWB system is used primary for sanity checksand provide boundary conditions of the UWB navigation algorithm.However, upon detecting a magnetic anomaly from the inertial system, themagnetic sensors data is temporary disabled from the inertial datafusion algorithm. The heading orientation is tracked only based on thegyroscopes estimation. The estimation of the heading orientation issubsequently corrected based on the UWB orientations calculation.

A proof of concept experiment was conducted to examine the orientationtracking of the UWB system with rigid body mechanics. FIG. 202 depictsthe experimental setup. Two units were used during the experiment. Forthe central unit, three off-the-shelf UWB anchors and an IMU system wererigidly fixed together as a reference. For the peripheral unit, threeoff-the-shelf UWB tags and an IMU system were rigidly fixed together asan active navigation unit. In the first experiment, the initialorientation between the UWB and IMU systems was registered together asthe initial orientation. The peripheral unit was rotated relative to thecentral unit and the orientations of each system were calculated anddepicted in FIG. 203. In the second experiment, both of the units werestationary. After the initial orientations of the units were registered,a ferromagnetic object was placed adjacent to the peripheral unit's IMUsystem to simulate a magnetic distortion situation. The 3D angles of theIMU system and the hybrid system of the initial and distortedenvironment is presented in FIG. 204.

Turning to FIG. 205, when used as a surgical navigation system, theexemplary hybrid system can provide full navigation capability to thesurgeon. The following outlines an exemplary application of theexemplary hybrid system for use with a total hip arthroplasty surgery.Preoperatively, the hip joint is imaged by an imaging modality. Theoutput from the imaging modality is used to create patient specificanatomical virtual models. These models may be created using X-ray threedimensional reconstruction, segmentation of CT scans or MM scans, or anyother imaging modality from which a three dimensional virtual model canbe created. Regardless of the approach taken to reach the patientspecific model, the models are used for planning and placing both theacetabular component and femoral stem. The surgical planning data alongwith patient acetabulum and femoral anatomy are imported into theexemplary hybrid system.

For the femoral registration, in one exemplary configuration of thishybrid system, a central unit is attached to a patient's femur as areference. A peripheral unit is attached to a mapping probe. In anotherexemplary configuration of this hybrid system, a central unit ispositioned adjacent to an operating table as a global reference. A firstperipheral unit is attached to a patient's femur, and a secondperipheral unit is attached to a mapping probe. Using eitherconfiguration, the patient's exposed femoral anatomical surface ismapped by painting the surface with the probe. The collected surfacepoints are registered with patient preoperative anatomical models. Thistranslates the preoperative femoral planning into the operating room andregisters it with the position of the patient's femur.

The registration of the patient's pelvis may take place afterregistration of the patient's femur. In one exemplary configuration ofthis hybrid system, a central unit is attached to the iliac crest of apatient's pelvis as a reference. A peripheral unit is attached to amapping probe (see FIG. 206). In another exemplary configuration of thishybrid system, a central unit is positioned adjacent to the operatingtable. A first peripheral unit is attached to a patient's pelvis, and asecond peripheral unit is attached to a mapping probe (see FIG. 207).Using either configuration, the patient's acetabular cup geometry ismapped by painting the surface with the probe. The collected surfacepoints are registered with patient preoperative anatomical models (seeFIG. 208). This translates the preoperative cup planning into theoperating room and registers it with the position of the patient'spelvis.

During the acetabular cup preparation, in one configuration of thishybrid system, a central unit is attached to the iliac crest of apatient's pelvis as a reference. A peripheral unit is attached to anacetabular reamer (see FIG. 209). In another alternate exemplaryconfiguration of this invention, a central unit is positioned adjacentto the operating table. A first peripheral unit is attached to the iliaccrest of a patient's pelvis, and a second peripheral unit is attached toan acetabular reamer. Using either configuration, the reaming directionis calculated by the differences between the relative orientationsbetween the central and peripheral units, and the planned acetabular cuporientations having been predetermined as part of the preoperativesurgical plan. In order to minimize error (e.g., deviation from thesurgical plan), the surgeon may maneuver the acetabular reamer based onfeedback from the surgical navigation guidance software indicatingwhether the position and orientation of the reamer coincide with thepreoperative surgical plan. The reaming direction guidance may beprovided to the surgeon via various viewing options such as 3D view, aclinical view, and multiple rendering options such as a computerrendering, an X-ray simulation, and a fluoroscopic simulation. Thereaming depth is calculated by translational distances between thecentral and peripheral units. The surgeon uses this information todetermine the reaming distance to avoid under or over reaming.

During the acetabular cup placement, in one configuration of this hybridsystem, a central unit is attached to the iliac crest of a patient'spelvis as a reference. A peripheral unit is attached to an acetabularshell inserter (see FIG. 210). In another alternate exemplaryconfiguration of this invention, a central unit is positioned adjacentto the operating table. A first peripheral unit is attached to the iliaccrest of a patient's pelvis, and a second peripheral unit is attached toan acetabular shell inserter. Using either configuration, the reamingdirection is calculated by the hybrid system using the differencesbetween the relative orientations between the central and peripheralunits, and the planned acetabular cup orientations predetermined via thepreoperative surgical plan. In order to minimize error (e.g., deviationfrom the surgical plan), the surgeon may maneuver the acetabularinserter based on the surgical navigation guidance software of thehybrid system. The direction of the acetabular cup placement may beprovided to the surgeon via various viewing options such as 3D view, aclinical view, and multiple rendering options such as a computerrendering, an X-ray simulation, and a fluoroscopic simulation. Theacetabular cup placement depth is calculated by translational distancesbetween the central and peripheral units. The surgeon uses thisinformation to determine the final acetabular cup placement.

During the femoral stem preparation, in one exemplary configuration ofthis hybrid system, a central unit is attached to a patient's femur as areference. A peripheral unit is attached to a femoral broach handle (seeFIG. 211). In another alternate exemplary configuration of thisinvention, a central unit is positioned adjacent to the operating table.A first peripheral unit is attached to a patient's femur, and a secondperipheral unit is attached to a femoral broach handle. Using eitherconfiguration, the broaching direction is calculated by the hybridsystem using the differences between the relative orientations betweenthe central and peripheral units, and the planned femoral stemorientations predetermined via the preoperative surgical plan. In orderto minimize error (e.g., deviation from the surgical plan), the surgeonmay maneuver the femoral broach based on the surgical navigationguidance software of the hybrid system. The broaching direction guidanceis provided to the surgeon via various viewing options such as 3D view,a clinical view, and multiple rendering options such as a computerrendering, an X-ray simulation, and a fluoroscopic simulation. Thebroaching depth is calculated by translational distances between thecentral and peripheral units. The surgeon uses this information todetermine the broached distance to avoid under or over rasping. Inaddition, the navigation software calculates and provides the overallleg length and offset based on the placement of the acetabular cup andthe femoral broached depth.

During the femoral stem placement, in one exemplary configuration ofthis hybrid system, a central unit is attached to a patient's femur as areference. A peripheral unit is attached to a femoral stem inserter. Inanother alternate exemplary configuration of this invention, a centralunit is positioned adjacent to the operating table. A first peripheralunit is attached to a patient's femur, and a second peripheral unit isattached to a femoral stem inserter. Using either configuration, theplacement direction is calculated by hybrid system using the differencesbetween the relative orientations between the central and peripheralunits, and the planned femoral stem orientations predetermined via thepreoperative surgical plan. In order to minimize error (e.g., deviationfrom the surgical plan), the surgeon may maneuver the femoral steminserter based on the surgical navigation guidance software. Thedirection of the femoral stem placement guidance is provided to thesurgeon via various viewing options such as 3D view, a clinical view,and multiple rendering options such as a computer rendering, an X-raysimulation, and a fluoroscopic simulation. The femoral placement depthis calculated by translational distances between the central andperipheral units. The surgeon uses this information to determine thefinal femoral stem placement. The navigation software calculates andprovides the overall leg length and offset.

The foregoing exemplary application of using the hybrid system during atotal hip arthroplasty procedure can be applied to any number of othersurgical procedures including, without limitation, total kneearthroplasty, total ankle arthroplasty, total shoulder arthroplasty,spinal surgery, open chest procedures, and minimally invasive surgicalprocedures. Moreover, the hybrid system may also be used as part of afully body suit for human motion tracking applications such as, withoutlimitation, biomechanics analysis (see FIG. 212).

Following from the above description and invention summaries, it shouldbe apparent to those of ordinary skill in the art that, while themethods and apparatuses herein described constitute exemplaryembodiments of the present invention, the invention contained herein isnot limited to this precise embodiment and that changes may be made tosuch embodiments without departing from the scope of the invention asdefined by the claims. Additionally, it is to be understood that theinvention is defined by the claims and it is not intended that anylimitations or elements describing the exemplary embodiments set forthherein are to be incorporated into the interpretation of any claimelement unless such limitation or element is explicitly stated.Likewise, it is to be understood that it is not necessary to meet any orall of the identified advantages or objects of the invention disclosedherein in order to fall within the scope of any claims, since theinvention is defined by the claims and since inherent and/or unforeseenadvantages of the present invention may exist even though they may nothave been explicitly discussed herein.

What is claimed is:
 1. A surgical navigation module comprising: amicrocomputer; a tri-axial accelerometer; a tri-axial gyroscope; atleast three tri-axial magnetometers; a communication module; anultrawide band transceiver; and, at least four ultrawide band antennas.2. The surgical navigation module of claim 1, further comprising amultiplexer.
 3. The surgical navigation module of claim 1, wherein themicrocomputer is programmed with a magnetic distortion algorithm toprocess inputs from the at least three tri-axial magnetometers toaccommodate for magnetic distortions.
 4. The surgical navigation moduleof claim 1, wherein the module includes a housing within which aremounted the microcomputer, the tri-axial accelerometer, the tri-axialgyroscope, the at least three tri-axial magnetometers, the communicationmodule, the ultrawide band transceiver, and the at least four ultrawideband antennas.
 5. The surgical navigation module of claim 1, wherein thetri-axial accelerometer comprises a plurality of tri-axialaccelerometers.
 6. The surgical navigation module of claim 1, whereinthe tri-axial gyroscope comprises a plurality of tri-axial gyroscopes.7. The surgical navigation module of claim 1, wherein the at least fourultrawide band antennas are equidistantly spaced from one another and donot lie along a common plane.
 8. The surgical navigation module of claim4, wherein the at least four ultrawide band antennas are rigidly mountedto the housing so an orientation of the at least four ultrawide bandantennas does not change with respect to one another.
 9. The surgicalnavigation module of claim 1, wherein the module is operative to recordchanges in at least six degrees of freedom.
 10. The surgical navigationmodule of claim 1, wherein the module is operative to determine its ownchanges in both translation and orientation.
 11. A surgical navigationmodule comprising: a microcomputer; an inertial sensing unit; anultrawide band unit; a housing containing the microcomputer, theinertial sensing module, and the ultrawide band module.
 12. The surgicalnavigation module of claim 11, wherein the inertial sensing unitcomprises: a tri-axial accelerometer; a tri-axial gyroscope; and, atleast three tri-axial magnetometers.
 13. The surgical navigation moduleof claim 11, wherein the ultrawide band unit comprises: an ultrawideband transceiver; and, at least four ultrawide band antennas.
 14. Thesurgical navigation module of claim 12, wherein the tri-axialaccelerometer comprises a plurality of tri-axial accelerometers.
 15. Thesurgical navigation module of claim 12, wherein the tri-axial gyroscopecomprises a plurality of tri-axial gyroscopes.
 16. The surgicalnavigation module of claim 13, wherein the at least four ultrawide bandantennas are equidistantly spaced from one another and do not lie alonga common plane.
 17. The surgical navigation module of claim 16, whereinthe at least four ultrawide band antennas are oriented in a tetrahedronorientation.
 18. The surgical navigation module of claim 13, wherein atleast three of the at least four ultrawide band antennas lie along acommon plane.
 19. The surgical navigation module of claim 13, whereinthe at least four ultrawide band antennas are rigidly mounted to thehousing so an orientation of the at least four ultrawide band antennasdoes not change with respect to one another.
 20. The surgical navigationmodule of claim 11, wherein the module is operative to record changes inat least six degrees of freedom.
 21. The surgical navigation module ofclaim 11, wherein the module is operative to determine its own changesin both translation and orientation.
 22. The surgical navigation moduleof claim 11, further comprising a multiplexer.
 23. The surgicalnavigation module of claim 11, wherein the microcomputer is programmedwith a magnetic distortion algorithm to process inputs from the at leastthree tri-axial magnetometers to accommodate for magnetic distortions.24. A surgical navigation system comprising a plurality of surgicalnavigation modules communicatively coupled to one another, each of theplurality of surgical navigation modules comprising: a microcomputer; atri-axial accelerometer; a tri-axial gyroscope; at least three tri-axialmagnetometers; a communication module; an ultrawide band transceiver;and, at least four ultrawide band antennas.
 25. The surgical navigationsystem of claim 24, wherein each of the plurality of surgical navigationmodules further includes a multiplexer.
 26. The surgical navigationsystem of claim 24, wherein the microcomputer is programmed with amagnetic distortion algorithm to process inputs from the at least threetri-axial magnetometers to accommodate for magnetic distortions.
 27. Thesurgical navigation system of claim 24, wherein each of the plurality ofsurgical navigation modules further includes a housing within which aremounted the microcomputer, the tri-axial accelerometer, the tri-axialgyroscope, the at least three tri-axial magnetometers, the communicationmodule, the ultrawide band transceiver, and the at least four ultrawideband antennas.
 28. The surgical navigation system of claim 24, whereinthe tri-axial accelerometer comprises a plurality of tri-axialaccelerometers.
 29. The surgical navigation system of claim 24, whereinthe tri-axial gyroscope comprises a plurality of tri-axial gyroscopes.30. The surgical navigation system of claim 24, wherein the at leastfour ultrawide band antennas are equidistantly spaced from one anotherand do not lie along a common plane. 31-116. (canceled)